{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用决策树模型完成分类问题\n",
    "\n",
    "#### 把需要的工具库import进来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:51:58.459827Z",
     "start_time": "2019-09-12T09:51:58.455839Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:51:59.691391Z",
     "start_time": "2019-09-12T09:51:59.632519Z"
    }
   },
   "outputs": [],
   "source": [
    "adult_data = pd.read_csv('./data/DecisionTree.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:00.379794Z",
     "start_time": "2019-09-12T09:52:00.368787Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           workclass   education       marital-status          occupation  \\\n",
       "0          State-gov   Bachelors        Never-married        Adm-clerical   \n",
       "1   Self-emp-not-inc   Bachelors   Married-civ-spouse     Exec-managerial   \n",
       "2            Private     HS-grad             Divorced   Handlers-cleaners   \n",
       "3            Private        11th   Married-civ-spouse   Handlers-cleaners   \n",
       "4            Private   Bachelors   Married-civ-spouse      Prof-specialty   \n",
       "\n",
       "     relationship    race   gender  native-country  income  \n",
       "0   Not-in-family   White     Male   United-States   <=50K  \n",
       "1         Husband   White     Male   United-States   <=50K  \n",
       "2   Not-in-family   White     Male   United-States   <=50K  \n",
       "3         Husband   Black     Male   United-States   <=50K  \n",
       "4            Wife   Black   Female            Cuba   <=50K  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取前5行\n",
    "adult_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:01.104713Z",
     "start_time": "2019-09-12T09:52:01.087758Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 9 columns):\n",
      "workclass         32561 non-null object\n",
      "education         32561 non-null object\n",
      "marital-status    32561 non-null object\n",
      "occupation        32561 non-null object\n",
      "relationship      32561 non-null object\n",
      "race              32561 non-null object\n",
      "gender            32561 non-null object\n",
      "native-country    32561 non-null object\n",
      "income            32561 non-null object\n",
      "dtypes: object(9)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "adult_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:01.859735Z",
     "start_time": "2019-09-12T09:52:01.855745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 9)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:02.422066Z",
     "start_time": "2019-09-12T09:52:02.418076Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['workclass', 'education', 'marital-status', 'occupation',\n",
       "       'relationship', 'race', 'gender', 'native-country', 'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 区分一下特征和目标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:03.411411Z",
     "start_time": "2019-09-12T09:52:03.408418Z"
    }
   },
   "outputs": [],
   "source": [
    "feature_columns = ['workclass', 'education', 'marital-status', 'occupation',\n",
    "       'relationship', 'race', 'gender', 'native-country']\n",
    "label_column = ['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:04.791289Z",
     "start_time": "2019-09-12T09:52:04.784758Z"
    }
   },
   "outputs": [],
   "source": [
    "features = adult_data[feature_columns]\n",
    "label = adult_data[label_column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:05.207760Z",
     "start_time": "2019-09-12T09:52:05.199786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           workclass   education       marital-status        occupation  \\\n",
       "0          State-gov   Bachelors        Never-married      Adm-clerical   \n",
       "1   Self-emp-not-inc   Bachelors   Married-civ-spouse   Exec-managerial   \n",
       "\n",
       "     relationship    race gender  native-country  \n",
       "0   Not-in-family   White   Male   United-States  \n",
       "1         Husband   White   Male   United-States  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:05.941218Z",
     "start_time": "2019-09-12T09:52:05.935236Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "   income\n",
       "0   <=50K\n",
       "1   <=50K"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 特征处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-11T13:56:15.442327Z",
     "start_time": "2019-09-11T13:56:15.438338Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function get_dummies in module pandas.core.reshape.reshape:\n",
      "\n",
      "get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)\n",
      "    Convert categorical variable into dummy/indicator variables\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    data : array-like, Series, or DataFrame\n",
      "    prefix : string, list of strings, or dict of strings, default None\n",
      "        String to append DataFrame column names.\n",
      "        Pass a list with length equal to the number of columns\n",
      "        when calling get_dummies on a DataFrame. Alternatively, `prefix`\n",
      "        can be a dictionary mapping column names to prefixes.\n",
      "    prefix_sep : string, default '_'\n",
      "        If appending prefix, separator/delimiter to use. Or pass a\n",
      "        list or dictionary as with `prefix.`\n",
      "    dummy_na : bool, default False\n",
      "        Add a column to indicate NaNs, if False NaNs are ignored.\n",
      "    columns : list-like, default None\n",
      "        Column names in the DataFrame to be encoded.\n",
      "        If `columns` is None then all the columns with\n",
      "        `object` or `category` dtype will be converted.\n",
      "    sparse : bool, default False\n",
      "        Whether the dummy-encoded columns should be be backed by\n",
      "        a :class:`SparseArray` (True) or a regular NumPy array (False).\n",
      "    drop_first : bool, default False\n",
      "        Whether to get k-1 dummies out of k categorical levels by removing the\n",
      "        first level.\n",
      "    \n",
      "        .. versionadded:: 0.18.0\n",
      "    \n",
      "    dtype : dtype, default np.uint8\n",
      "        Data type for new columns. Only a single dtype is allowed.\n",
      "    \n",
      "        .. versionadded:: 0.23.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    dummies : DataFrame\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    Series.str.get_dummies\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> s = pd.Series(list('abca'))\n",
      "    \n",
      "    >>> pd.get_dummies(s)\n",
      "       a  b  c\n",
      "    0  1  0  0\n",
      "    1  0  1  0\n",
      "    2  0  0  1\n",
      "    3  1  0  0\n",
      "    \n",
      "    >>> s1 = ['a', 'b', np.nan]\n",
      "    \n",
      "    >>> pd.get_dummies(s1)\n",
      "       a  b\n",
      "    0  1  0\n",
      "    1  0  1\n",
      "    2  0  0\n",
      "    \n",
      "    >>> pd.get_dummies(s1, dummy_na=True)\n",
      "       a  b  NaN\n",
      "    0  1  0    0\n",
      "    1  0  1    0\n",
      "    2  0  0    1\n",
      "    \n",
      "    >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],\n",
      "    ...                    'C': [1, 2, 3]})\n",
      "    \n",
      "    >>> pd.get_dummies(df, prefix=['col1', 'col2'])\n",
      "       C  col1_a  col1_b  col2_a  col2_b  col2_c\n",
      "    0  1       1       0       0       1       0\n",
      "    1  2       0       1       1       0       0\n",
      "    2  3       1       0       0       0       1\n",
      "    \n",
      "    >>> pd.get_dummies(pd.Series(list('abcaa')))\n",
      "       a  b  c\n",
      "    0  1  0  0\n",
      "    1  0  1  0\n",
      "    2  0  0  1\n",
      "    3  1  0  0\n",
      "    4  1  0  0\n",
      "    \n",
      "    >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)\n",
      "       b  c\n",
      "    0  0  0\n",
      "    1  1  0\n",
      "    2  0  1\n",
      "    3  0  0\n",
      "    4  0  0\n",
      "    \n",
      "    >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)\n",
      "         a    b    c\n",
      "    0  1.0  0.0  0.0\n",
      "    1  0.0  1.0  0.0\n",
      "    2  0.0  0.0  1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.get_dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:33.857356Z",
     "start_time": "2019-09-12T09:52:33.824302Z"
    }
   },
   "outputs": [],
   "source": [
    "# pd.get_dummies 相当于对类别特征 one-hot 编码\n",
    "features = pd.get_dummies(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:52:34.714378Z",
     "start_time": "2019-09-12T09:52:34.703407Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass_ ?</th>\n",
       "      <th>workclass_ Federal-gov</th>\n",
       "      <th>workclass_ Local-gov</th>\n",
       "      <th>workclass_ Never-worked</th>\n",
       "      <th>workclass_ Private</th>\n",
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       "      <th>...</th>\n",
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       "      <th>native-country_ Thailand</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   workclass_ ?  workclass_ Federal-gov  workclass_ Local-gov  \\\n",
       "0             0                       0                     0   \n",
       "1             0                       0                     0   \n",
       "\n",
       "   workclass_ Never-worked  workclass_ Private  workclass_ Self-emp-inc  \\\n",
       "0                        0                   0                        0   \n",
       "1                        0                   0                        0   \n",
       "\n",
       "   workclass_ Self-emp-not-inc  workclass_ State-gov  workclass_ Without-pay  \\\n",
       "0                            0                     1                       0   \n",
       "1                            1                     0                       0   \n",
       "\n",
       "   education_ 10th  ...  native-country_ Portugal  \\\n",
       "0                0  ...                         0   \n",
       "1                0  ...                         0   \n",
       "\n",
       "   native-country_ Puerto-Rico  native-country_ Scotland  \\\n",
       "0                            0                         0   \n",
       "1                            0                         0   \n",
       "\n",
       "   native-country_ South  native-country_ Taiwan  native-country_ Thailand  \\\n",
       "0                      0                       0                         0   \n",
       "1                      0                       0                         0   \n",
       "\n",
       "   native-country_ Trinadad&Tobago  native-country_ United-States  \\\n",
       "0                                0                              1   \n",
       "1                                0                              1   \n",
       "\n",
       "   native-country_ Vietnam  native-country_ Yugoslavia  \n",
       "0                        0                           0  \n",
       "1                        0                           0  \n",
       "\n",
       "[2 rows x 102 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-11T13:56:16.636144Z",
     "start_time": "2019-09-11T13:56:16.630160Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class DecisionTreeClassifier in module sklearn.tree.tree:\n",
      "\n",
      "class DecisionTreeClassifier(BaseDecisionTree, sklearn.base.ClassifierMixin)\n",
      " |  DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)\n",
      " |  \n",
      " |  A decision tree classifier.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <tree>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  criterion : string, optional (default=\"gini\")\n",
      " |      The function to measure the quality of a split. Supported criteria are\n",
      " |      \"gini\" for the Gini impurity and \"entropy\" for the information gain.\n",
      " |  \n",
      " |  splitter : string, optional (default=\"best\")\n",
      " |      The strategy used to choose the split at each node. Supported\n",
      " |      strategies are \"best\" to choose the best split and \"random\" to choose\n",
      " |      the best random split.\n",
      " |  \n",
      " |  max_depth : int or None, optional (default=None)\n",
      " |      The maximum depth of the tree. If None, then nodes are expanded until\n",
      " |      all leaves are pure or until all leaves contain less than\n",
      " |      min_samples_split samples.\n",
      " |  \n",
      " |  min_samples_split : int, float, optional (default=2)\n",
      " |      The minimum number of samples required to split an internal node:\n",
      " |  \n",
      " |      - If int, then consider `min_samples_split` as the minimum number.\n",
      " |      - If float, then `min_samples_split` is a fraction and\n",
      " |        `ceil(min_samples_split * n_samples)` are the minimum\n",
      " |        number of samples for each split.\n",
      " |  \n",
      " |      .. versionchanged:: 0.18\n",
      " |         Added float values for fractions.\n",
      " |  \n",
      " |  min_samples_leaf : int, float, optional (default=1)\n",
      " |      The minimum number of samples required to be at a leaf node.\n",
      " |      A split point at any depth will only be considered if it leaves at\n",
      " |      least ``min_samples_leaf`` training samples in each of the left and\n",
      " |      right branches.  This may have the effect of smoothing the model,\n",
      " |      especially in regression.\n",
      " |  \n",
      " |      - If int, then consider `min_samples_leaf` as the minimum number.\n",
      " |      - If float, then `min_samples_leaf` is a fraction and\n",
      " |        `ceil(min_samples_leaf * n_samples)` are the minimum\n",
      " |        number of samples for each node.\n",
      " |  \n",
      " |      .. versionchanged:: 0.18\n",
      " |         Added float values for fractions.\n",
      " |  \n",
      " |  min_weight_fraction_leaf : float, optional (default=0.)\n",
      " |      The minimum weighted fraction of the sum total of weights (of all\n",
      " |      the input samples) required to be at a leaf node. Samples have\n",
      " |      equal weight when sample_weight is not provided.\n",
      " |  \n",
      " |  max_features : int, float, string or None, optional (default=None)\n",
      " |      The number of features to consider when looking for the best split:\n",
      " |  \n",
      " |          - If int, then consider `max_features` features at each split.\n",
      " |          - If float, then `max_features` is a fraction and\n",
      " |            `int(max_features * n_features)` features are considered at each\n",
      " |            split.\n",
      " |          - If \"auto\", then `max_features=sqrt(n_features)`.\n",
      " |          - If \"sqrt\", then `max_features=sqrt(n_features)`.\n",
      " |          - If \"log2\", then `max_features=log2(n_features)`.\n",
      " |          - If None, then `max_features=n_features`.\n",
      " |  \n",
      " |      Note: the search for a split does not stop until at least one\n",
      " |      valid partition of the node samples is found, even if it requires to\n",
      " |      effectively inspect more than ``max_features`` features.\n",
      " |  \n",
      " |  random_state : int, RandomState instance or None, optional (default=None)\n",
      " |      If int, random_state is the seed used by the random number generator;\n",
      " |      If RandomState instance, random_state is the random number generator;\n",
      " |      If None, the random number generator is the RandomState instance used\n",
      " |      by `np.random`.\n",
      " |  \n",
      " |  max_leaf_nodes : int or None, optional (default=None)\n",
      " |      Grow a tree with ``max_leaf_nodes`` in best-first fashion.\n",
      " |      Best nodes are defined as relative reduction in impurity.\n",
      " |      If None then unlimited number of leaf nodes.\n",
      " |  \n",
      " |  min_impurity_decrease : float, optional (default=0.)\n",
      " |      A node will be split if this split induces a decrease of the impurity\n",
      " |      greater than or equal to this value.\n",
      " |  \n",
      " |      The weighted impurity decrease equation is the following::\n",
      " |  \n",
      " |          N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
      " |                              - N_t_L / N_t * left_impurity)\n",
      " |  \n",
      " |      where ``N`` is the total number of samples, ``N_t`` is the number of\n",
      " |      samples at the current node, ``N_t_L`` is the number of samples in the\n",
      " |      left child, and ``N_t_R`` is the number of samples in the right child.\n",
      " |  \n",
      " |      ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n",
      " |      if ``sample_weight`` is passed.\n",
      " |  \n",
      " |      .. versionadded:: 0.19\n",
      " |  \n",
      " |  min_impurity_split : float, (default=1e-7)\n",
      " |      Threshold for early stopping in tree growth. A node will split\n",
      " |      if its impurity is above the threshold, otherwise it is a leaf.\n",
      " |  \n",
      " |      .. deprecated:: 0.19\n",
      " |         ``min_impurity_split`` has been deprecated in favor of\n",
      " |         ``min_impurity_decrease`` in 0.19. The default value of\n",
      " |         ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it\n",
      " |         will be removed in 0.25. Use ``min_impurity_decrease`` instead.\n",
      " |  \n",
      " |  class_weight : dict, list of dicts, \"balanced\" or None, default=None\n",
      " |      Weights associated with classes in the form ``{class_label: weight}``.\n",
      " |      If not given, all classes are supposed to have weight one. For\n",
      " |      multi-output problems, a list of dicts can be provided in the same\n",
      " |      order as the columns of y.\n",
      " |  \n",
      " |      Note that for multioutput (including multilabel) weights should be\n",
      " |      defined for each class of every column in its own dict. For example,\n",
      " |      for four-class multilabel classification weights should be\n",
      " |      [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of\n",
      " |      [{1:1}, {2:5}, {3:1}, {4:1}].\n",
      " |  \n",
      " |      The \"balanced\" mode uses the values of y to automatically adjust\n",
      " |      weights inversely proportional to class frequencies in the input data\n",
      " |      as ``n_samples / (n_classes * np.bincount(y))``\n",
      " |  \n",
      " |      For multi-output, the weights of each column of y will be multiplied.\n",
      " |  \n",
      " |      Note that these weights will be multiplied with sample_weight (passed\n",
      " |      through the fit method) if sample_weight is specified.\n",
      " |  \n",
      " |  presort : bool, optional (default=False)\n",
      " |      Whether to presort the data to speed up the finding of best splits in\n",
      " |      fitting. For the default settings of a decision tree on large\n",
      " |      datasets, setting this to true may slow down the training process.\n",
      " |      When using either a smaller dataset or a restricted depth, this may\n",
      " |      speed up the training.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  classes_ : array of shape = [n_classes] or a list of such arrays\n",
      " |      The classes labels (single output problem),\n",
      " |      or a list of arrays of class labels (multi-output problem).\n",
      " |  \n",
      " |  feature_importances_ : array of shape = [n_features]\n",
      " |      The feature importances. The higher, the more important the\n",
      " |      feature. The importance of a feature is computed as the (normalized)\n",
      " |      total reduction of the criterion brought by that feature.  It is also\n",
      " |      known as the Gini importance [4]_.\n",
      " |  \n",
      " |  max_features_ : int,\n",
      " |      The inferred value of max_features.\n",
      " |  \n",
      " |  n_classes_ : int or list\n",
      " |      The number of classes (for single output problems),\n",
      " |      or a list containing the number of classes for each\n",
      " |      output (for multi-output problems).\n",
      " |  \n",
      " |  n_features_ : int\n",
      " |      The number of features when ``fit`` is performed.\n",
      " |  \n",
      " |  n_outputs_ : int\n",
      " |      The number of outputs when ``fit`` is performed.\n",
      " |  \n",
      " |  tree_ : Tree object\n",
      " |      The underlying Tree object. Please refer to\n",
      " |      ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and\n",
      " |      :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`\n",
      " |      for basic usage of these attributes.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  The default values for the parameters controlling the size of the trees\n",
      " |  (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n",
      " |  unpruned trees which can potentially be very large on some data sets. To\n",
      " |  reduce memory consumption, the complexity and size of the trees should be\n",
      " |  controlled by setting those parameter values.\n",
      " |  \n",
      " |  The features are always randomly permuted at each split. Therefore,\n",
      " |  the best found split may vary, even with the same training data and\n",
      " |  ``max_features=n_features``, if the improvement of the criterion is\n",
      " |  identical for several splits enumerated during the search of the best\n",
      " |  split. To obtain a deterministic behaviour during fitting,\n",
      " |  ``random_state`` has to be fixed.\n",
      " |  \n",
      " |  See also\n",
      " |  --------\n",
      " |  DecisionTreeRegressor\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  \n",
      " |  .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning\n",
      " |  \n",
      " |  .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, \"Classification\n",
      " |         and Regression Trees\", Wadsworth, Belmont, CA, 1984.\n",
      " |  \n",
      " |  .. [3] T. Hastie, R. Tibshirani and J. Friedman. \"Elements of Statistical\n",
      " |         Learning\", Springer, 2009.\n",
      " |  \n",
      " |  .. [4] L. Breiman, and A. Cutler, \"Random Forests\",\n",
      " |         https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn.datasets import load_iris\n",
      " |  >>> from sklearn.model_selection import cross_val_score\n",
      " |  >>> from sklearn.tree import DecisionTreeClassifier\n",
      " |  >>> clf = DecisionTreeClassifier(random_state=0)\n",
      " |  >>> iris = load_iris()\n",
      " |  >>> cross_val_score(clf, iris.data, iris.target, cv=10)\n",
      " |  ...                             # doctest: +SKIP\n",
      " |  ...\n",
      " |  array([ 1.     ,  0.93...,  0.86...,  0.93...,  0.93...,\n",
      " |          0.93...,  0.93...,  1.     ,  0.93...,  1.      ])\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      DecisionTreeClassifier\n",
      " |      BaseDecisionTree\n",
      " |      abc.NewBase\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None)\n",
      " |      Build a decision tree classifier from the training set (X, y).\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix, shape = [n_samples, n_features]\n",
      " |          The training input samples. Internally, it will be converted to\n",
      " |          ``dtype=np.float32`` and if a sparse matrix is provided\n",
      " |          to a sparse ``csc_matrix``.\n",
      " |      \n",
      " |      y : array-like, shape = [n_samples] or [n_samples, n_outputs]\n",
      " |          The target values (class labels) as integers or strings.\n",
      " |      \n",
      " |      sample_weight : array-like, shape = [n_samples] or None\n",
      " |          Sample weights. If None, then samples are equally weighted. Splits\n",
      " |          that would create child nodes with net zero or negative weight are\n",
      " |          ignored while searching for a split in each node. Splits are also\n",
      " |          ignored if they would result in any single class carrying a\n",
      " |          negative weight in either child node.\n",
      " |      \n",
      " |      check_input : boolean, (default=True)\n",
      " |          Allow to bypass several input checking.\n",
      " |          Don't use this parameter unless you know what you do.\n",
      " |      \n",
      " |      X_idx_sorted : array-like, shape = [n_samples, n_features], optional\n",
      " |          The indexes of the sorted training input samples. If many tree\n",
      " |          are grown on the same dataset, this allows the ordering to be\n",
      " |          cached between trees. If None, the data will be sorted here.\n",
      " |          Don't use this parameter unless you know what to do.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |  \n",
      " |  predict_log_proba(self, X)\n",
      " |      Predict class log-probabilities of the input samples X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix of shape = [n_samples, n_features]\n",
      " |          The input samples. Internally, it will be converted to\n",
      " |          ``dtype=np.float32`` and if a sparse matrix is provided\n",
      " |          to a sparse ``csr_matrix``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      p : array of shape = [n_samples, n_classes], or a list of n_outputs\n",
      " |          such arrays if n_outputs > 1.\n",
      " |          The class log-probabilities of the input samples. The order of the\n",
      " |          classes corresponds to that in the attribute `classes_`.\n",
      " |  \n",
      " |  predict_proba(self, X, check_input=True)\n",
      " |      Predict class probabilities of the input samples X.\n",
      " |      \n",
      " |      The predicted class probability is the fraction of samples of the same\n",
      " |      class in a leaf.\n",
      " |      \n",
      " |      check_input : boolean, (default=True)\n",
      " |          Allow to bypass several input checking.\n",
      " |          Don't use this parameter unless you know what you do.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix of shape = [n_samples, n_features]\n",
      " |          The input samples. Internally, it will be converted to\n",
      " |          ``dtype=np.float32`` and if a sparse matrix is provided\n",
      " |          to a sparse ``csr_matrix``.\n",
      " |      \n",
      " |      check_input : bool\n",
      " |          Run check_array on X.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      p : array of shape = [n_samples, n_classes], or a list of n_outputs\n",
      " |          such arrays if n_outputs > 1.\n",
      " |          The class probabilities of the input samples. The order of the\n",
      " |          classes corresponds to that in the attribute `classes_`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from BaseDecisionTree:\n",
      " |  \n",
      " |  apply(self, X, check_input=True)\n",
      " |      Returns the index of the leaf that each sample is predicted as.\n",
      " |      \n",
      " |      .. versionadded:: 0.17\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array_like or sparse matrix, shape = [n_samples, n_features]\n",
      " |          The input samples. Internally, it will be converted to\n",
      " |          ``dtype=np.float32`` and if a sparse matrix is provided\n",
      " |          to a sparse ``csr_matrix``.\n",
      " |      \n",
      " |      check_input : boolean, (default=True)\n",
      " |          Allow to bypass several input checking.\n",
      " |          Don't use this parameter unless you know what you do.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_leaves : array_like, shape = [n_samples,]\n",
      " |          For each datapoint x in X, return the index of the leaf x\n",
      " |          ends up in. Leaves are numbered within\n",
      " |          ``[0; self.tree_.node_count)``, possibly with gaps in the\n",
      " |          numbering.\n",
      " |  \n",
      " |  decision_path(self, X, check_input=True)\n",
      " |      Return the decision path in the tree\n",
      " |      \n",
      " |      .. versionadded:: 0.18\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array_like or sparse matrix, shape = [n_samples, n_features]\n",
      " |          The input samples. Internally, it will be converted to\n",
      " |          ``dtype=np.float32`` and if a sparse matrix is provided\n",
      " |          to a sparse ``csr_matrix``.\n",
      " |      \n",
      " |      check_input : boolean, (default=True)\n",
      " |          Allow to bypass several input checking.\n",
      " |          Don't use this parameter unless you know what you do.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      indicator : sparse csr array, shape = [n_samples, n_nodes]\n",
      " |          Return a node indicator matrix where non zero elements\n",
      " |          indicates that the samples goes through the nodes.\n",
      " |  \n",
      " |  predict(self, X, check_input=True)\n",
      " |      Predict class or regression value for X.\n",
      " |      \n",
      " |      For a classification model, the predicted class for each sample in X is\n",
      " |      returned. For a regression model, the predicted value based on X is\n",
      " |      returned.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix of shape = [n_samples, n_features]\n",
      " |          The input samples. Internally, it will be converted to\n",
      " |          ``dtype=np.float32`` and if a sparse matrix is provided\n",
      " |          to a sparse ``csr_matrix``.\n",
      " |      \n",
      " |      check_input : boolean, (default=True)\n",
      " |          Allow to bypass several input checking.\n",
      " |          Don't use this parameter unless you know what you do.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y : array of shape = [n_samples] or [n_samples, n_outputs]\n",
      " |          The predicted classes, or the predict values.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from BaseDecisionTree:\n",
      " |  \n",
      " |  feature_importances_\n",
      " |      Return the feature importances.\n",
      " |      \n",
      " |      The importance of a feature is computed as the (normalized) total\n",
      " |      reduction of the criterion brought by that feature.\n",
      " |      It is also known as the Gini importance.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      feature_importances_ : array, shape = [n_features]\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : boolean, optional\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Returns the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape = (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like, shape = (n_samples) or (n_samples, n_outputs)\n",
      " |          True labels for X.\n",
      " |      \n",
      " |      sample_weight : array-like, shape = [n_samples], optional\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of self.predict(X) wrt. y.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(tree.DecisionTreeClassifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:01:57.921140Z",
     "start_time": "2019-09-12T11:01:57.837217Z"
    }
   },
   "outputs": [],
   "source": [
    "# 初始化一个决策树分类器, 使用信息增益作为特征选择指标\n",
    "clf = tree.DecisionTreeClassifier(criterion='entropy',max_depth=4)\n",
    "# 拟合数据\n",
    "clf = clf.fit(features, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:01:58.442424Z",
     "start_time": "2019-09-12T11:01:58.428461Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' <=50K', ' <=50K', ..., ' <=50K', ' <=50K', ' >50K'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在训练数据上预测\n",
    "clf.predict(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化决策树\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:55:11.886074Z",
     "start_time": "2019-09-12T09:55:10.466648Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydotplus in c:\\users\\xukaihui\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (2.0.2)\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in c:\\users\\xukaihui\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from pydotplus) (2.3.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:55:13.141387Z",
     "start_time": "2019-09-12T09:55:13.138395Z"
    }
   },
   "outputs": [],
   "source": [
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T09:55:14.051785Z",
     "start_time": "2019-09-12T09:55:14.048794Z"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.display import display, Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:02:01.717198Z",
     "start_time": "2019-09-12T11:02:01.710217Z"
    }
   },
   "outputs": [],
   "source": [
    "dot_dat = tree.export_graphviz(clf,\n",
    "                               out_file=None,\n",
    "                               feature_names=features.columns,\n",
    "                               class_names = ['<=50k', '>50k'],\n",
    "                               filled = True,\n",
    "                               rounded =True\n",
    "                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:02:02.769100Z",
     "start_time": "2019-09-12T11:02:02.765111Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    " \n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:02:03.628624Z",
     "start_time": "2019-09-12T11:02:03.276536Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = pydotplus.graph_from_dot_data(dot_dat)\n",
    "display(Image(graph.create_png()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分类决策树如何处理类别特征实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:02:16.593413Z",
     "start_time": "2019-09-12T11:02:16.589423Z"
    }
   },
   "outputs": [],
   "source": [
    "# 初始化一个决策树分类器, 使用信息增益作为特征选择指标\n",
    "clf = tree.DecisionTreeClassifier(criterion='entropy',max_depth=4)\n",
    "# 拟合数据\n",
    "clf = clf.fit(np.array([[0,2,0],[1,2,1],[2,3,5]]), np.array([1,0,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:02:17.569313Z",
     "start_time": "2019-09-12T11:02:17.565295Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(np.array([[0,1,0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:02:18.194752Z",
     "start_time": "2019-09-12T11:02:18.189766Z"
    }
   },
   "outputs": [],
   "source": [
    "dot_dat = tree.export_graphviz(clf,\n",
    "                               out_file=None,\n",
    "                               feature_names=['f1','f2','f3'],\n",
    "                               class_names = ['0','1'],\n",
    "                               filled = True,\n",
    "                               rounded =True\n",
    "                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-12T11:02:20.070202Z",
     "start_time": "2019-09-12T11:02:19.933568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VR5++vlyvWx1j7nHk2qUXJi7eiMzLv2v85keINaMavgVhyiyXTh5k91h369kfEVdKFTYpY24uNmvTAXezrkGSHI/kA7s1Hlddv1xAseeutqTKpd+tMn36+nK9blPr0n8IRkwLROCX+zFdtB1hy2ch4+IZs52fEFOjGb4F+WCBCJLkeIW6vKaat3K5RBtN2zFz3b6XS79bZfr09dXnupWZql9u/xHj8WPwQpzc863ZSkqEmBolfAvSzr0nIq6UKtyQZfZY/2CBiC2BMCWVob6z0W/4B6jv3BiNmrbE4vc68nwFhuF63ebEfDOkNoXEllDCt0Dt3HuinXtP9Bs+DgV3byFknhCS5Hh2pso8JCQ/u+bS+tBQXPvdyjOkr6+u61anujV8Tf1umdaU6vr/EmKtqIZvQX7atBhz+jREzrWLAKpKJ83baZ61M4mXuRlqKlz73crTp6+vvtdtTOr63T5/WspeI3MdhNgCmuFbkLeEU5AUF4lNM7xU3psu+iehM03EV47ro/Y4+bnZKnX36uLa75ahT19frtdtCgNG+uJCfKzafre6rpEQa0MJ34K49eyvsj5+zJyleL1HX4XG4wNG+uLFs6cK/Vk9Rvuh7OVzrPMbjMzLvxs14evT71b5c1z6+nK9blMJ/HI/Uo/HsX1pmXsjlOyJraGetjrYek9bbQzpd0sMRz1tialRDZ8QQuwEJXxCCLETlPAJIcRO0E1bohHV7gmxLTTDJ4QQO0EzfBtkratrlPfFURc/s5Vyda/t+dNSXDt7gl2KKfD0hsDTW2HJKJd4CLEmlPCJ1bibdU1l33xDPH9aigiRv8I+Ocxe/ZLkeMxc/bXGDecIsWaU8InFUTeTZvbHN4ZrZ0+wfWz7jxivsld/yi/7MGJaIBsHlx05CbEGVMMnFu9E1A5smuFltJaDTB9bz/Ez1e7Vz2zTTIitoRm+BZjTpyGG+s5Wu7f8T5sWK+w6yaWXrbrjA6ozZ02vc+1Dq+k82hhSB4/ZthKBX+6HwNNbodWioTS1ZuS6qych1ooSvgWYuHgjYratxNiAFQq14ydFj5AUF4mJizeiXoOGeveyNQTXPrTmZK6bpczuo9S8nNgqSvgWoOvAoQCA9ItnFJJ2+v/a6zEbiOnby1Zf+vShVcfaV7Gk/LIPAk9v9Bw8gu9QCDEJquFbgHbuPSHw9GZry4wL8bEY6jub3flS3162+tKnD62tYX6y+WCBiEo7xGbRDN9CDJ+6ACHzhOxe9vm52ZAkxyNol1hhnD69bPWlTx9adUxVwzc15s90zb6zvLRTJMRcKOFbiPZd3wQAdi/73Iw0hdcB2+tly7cnRY+QsG8X7mZdw8aDV4zeNIYQS0MJ30LUa9AQ00Xb8WPwQrw5dDTCls/CdNF2hfKCMXvZyrcdZBjSh1aeJc7eNbmbdQ2Hvg1GO/ee9KAVsRtUw7cgb/R9GwDYGXuPt95TO07fXrbMTV+mZ+zzp6VI2LdLZZw+fWitWdFf97DObzDauffEBwtElOyJ3aAZvgVp0b4TO8se6jsbjVsq1tIN7WU70HsCJMnxCk+qTly8UWWcPn1oLRHXPYT+PHcKANReJ8OaflohhCua4VsYZpb9lnCKynsDRvoqNPUeM2cpNh68gjX7zgKoqv+rM2CkL+Zu/o6d6U8Xbdd48/WDBSLM3fwdhvrOZl+bLtpuU2UP5WblhNgL6mmrgz33tDU3Y+zyOadPQ6P/XZlr91HqaUtMjWb4xGbkXLuo8BMQIUQR1fCJxTF0Rp2ddl7rcwKGxkGIraAZPrEZxkz2hNgimuETi2Fp90ksLR5Cqotm+IQQYico4duROX0a2k1dWpIcbzfXSghXlPCJzTFW71tCbA0lfGJTcq5dxDq/wXyHQYhFooRPbIaxe98SYmtolY6NeP60FNfOnsCF+FhIkuMx1Hc2hk/9SOeWv1x75Mr3uQWqtnVQ1wGL6zhlxthL39i9bwmxNZTwbUSEyJ/tbQsASXGRSIqL1NrUg2uPXHXjmI3HgnaJ2QbnXMeZCi2jJEQ7Svg2QL7RONOeMPV4HMKWz8KZuO8U9s+Xx7VHLjNuy7Eb7A6eOdcuYtMML1w6eYhN5FzHqUPJmhDTo4RvA679fgIA4OU3j21cMmCkr86m5kySfVL0CHezrqHor3u4/edllXECT29IkuNx6eRBuHbphfZd34Rbz/4qSZrrOEIIPyjh69CgQQMAwKuyF6hVuy7P0ajH1MsN2b6YS4/cDxaIIEmOV6jzD5+6QGXGznWcOtbaD9eYXv79FI5Nm/IdBrFhlPB1aNKkCQDgmbQYjZq14jka4+LaI7ede09EXClVuMErSY6HwNMbHywQsfcIuI4j6j0tLkSTN9/gOwxiwyjh69CtWzcAwINbGRab8JkuWU+KHuk1y9e3R247955o594T/YaPQ8HdWwiZJ4QkOV5l5s11nDxbn71z8fB2Jrp2ncN3GMSG0Tp8HVxcXNCtR09kXT3HdygaufetetAoYd8uNmGnHo/DnD4N8dOmxTo/r6tH7k+bFmNOn4ZsT9zGLduiebuOBo8jqu5lX8ezJ6Xw9PTkOxRiw2iGz8H4D8bix5iD+GD+Sr5DUWvASF9ciI9V26N1iK/m9ehce+S+JZyCpLhIhZ64DPmGI1zHEVVXE8Xo/EYXdOnShe9QiA2jGT4Hc+bMwb1bGbj1RyrfoWg0Jzhcbb9bbXVzrj1y3Xr2x5p9ZzFmzlKFsYFf7ofn+Jnsa1zHEUWVlRU4dyQK8+fN5TsUYuOopy1Hc+cF4FxaOhaHHuU7FGJjkmIjcPqnL5GVkY7XXnuN73CIDaMZPkcbgzfgbuYfuHzqEN+hEBvyVFqEI6HB2PLFZkr2xOQo4XPUrFkzbFi/Dnu/+BSF93P5DofYgMrKCvywbgF6dO+OyZMn8x0OsQNU0tFDeXk5ho8YiZz7+fgs4jj7VCshhojdthLnDv+ISxdT4e7uznc4xA7QDF8PNWvWxKGDB1Bb9grfLpmCF8+e8h0SsVK//vAVEvfvwqGDByjZE7OhhK8nZ2dnxB/7BcX3svHvWcNR9Nc9vkMiVqSyohw/bVqMg1+vxbfffINhw4bxHRKxI5TwDdCpUydcupiKpk51sXn6u7h29iTfIRErUPggD19+PB6Xfo2BWCzGnDn0VC0xL0r4BmrTpg1+Sz6Dke8Nw1eBH2LHwgkouJvDd1jEApW9eI6D32zAGt/+qJDm49y5s/D29uY7LGKH6KatESQlJeGjjwORlZmJN4cJ8daYKegywNNid9ck5pGXIcGlU4dw9vCPkJW/wupVIgQGBqJ27dp8h0bsFCV8IykvL8f+/fuxKywc587+DgeHGmjj9gYaNm2JOvWd+A4PlZUVqFHDke8wTKayogI1HPm/vvKyF/hbWoT7ORl4VipFh9fdMOv/ZmLevHlo3rw53+ERO0cJ3wQKCwtx+vRpSCQSPHz4EE+ePOE7JKSlpeHp06d4++23+Q7F6G7fvo309HS8//77qFGD3ypl3bp10bhxY3Tv3h3vvPMO7Y1DLAolfDvw4MEDdOrUCf/+978RGBjIdzhGl5eXh86dO2P79u2YN28e3+EQYrEo4duBhQsX4sCBA8jOzkbdurZ5X2HBggX45ZdfcPPmTaqRE6IBrdKxcffv30d4eDiWL19us8keAFasWIH8/HxERkbyHQohFotm+Dbu448/xuHDh5GdnY06derwHY5J2dO1EmIImuHbsHv37iEiIgIrVqywiwS4fPlyFBYWIiIigu9QCLFINMO3YfZY17aH+xWEGIpm+DYqLy8PkZGRWLFihd0kewBYtmwZioqKEB4ernOsg4ODzl/6MvRzhJgDzfBtVEBAAI4fP47MzEy7SvgAsGjRIsTGxiI7Oxv16tXTOI5LYtb3vwdzTPpvRSwRzfBt0J07d/D999/b3eyesWzZMhQXFyMsLIzTeJlMpvEXIbaEZvg2aO7cuTh16hQyMzNRq1YtvsPhxZIlS7B3717k5ORonOWbYjZOM3xiyWiGb2Nu376N3bt3Y+XKlXab7AFg6dKlKC0txc6dO412TIlEgq1bt7J1eh8fH+zbt0/n5xITEzF//nz2c6tWrYJEItE51sfHB4mJiUaLnxDIiE2ZPXu2zM3NTfbq1Su+Q+FdUFCQrEWLFrJnz56pfR+AjOt/gSNHjrDjlX9FR0drPKa2zyUkJCicQyQSqR0nEokMuHpCVFHCtyG3bt2S1axZU/b999/zHYpFyM/Pl9WvX1/2n//8R+37mhKxum8EzGspKSnsa7m5uSpjNX2dm5vLvpaSkiIDIAsICGBfS0hIYJN7SUmJTCaTyUpKSthvAmlpadX7wyBEJpNRSceGbNiwAR06dMC//vUvvkOxCM2bN8dHH32E//znP3j27Fm1jiX7301cNzc3SCQSiMViTks/hUIhACA2NhaJiYmQSqUYNGgQZDKZQrnp9OnTAICgoCA4OzsDqGqnGRQUBAA4depUteInBACVdGzFzZs3ZTVr1pT98MMPfIdiUQoKCmQNGjSQ/fvf/1Z5D3qUdGQyzSUXaJnhp6WlKYwTCoUqpRz5z2n7RUh10b8iGzF9+nSZu7s71e7VWLZsmaxZs2ayJ0+eKLyuTyINCwtjyzAJCQmytLQ0WX5+vs6Ez0hLS5OFhIQoJH75Mg0lfGIOtCwT3B7AASx3qV1WVha6d++O77//nso5ajx+/Bivv/46VqxYgWXLlrGv67OEUt1YqVSKRo0aKbyu65h5eXnIzs6Gl5eXwrj58+cjNDQUJSUlbEmHEGOjGr4N2LBhAzp27IjJkyfzHYpFatKkCQIDAxESElLt7mNZWVkAqpJ9SEiIzvHMEsvz588DAFxdXdGpUyeVcRMmTAAAhISEoKCggH09MTERDg4O2Lp1a7XiJgQA/ZyoDqzoR+iMjAyZo6OjbM+ePXyHYtEeP34sa9iwoWzjxo3sa/r8PUdHR2stt2RmZqo9JrMiR92vsLAwhXNoukcgFApl+fn5RvhTIPbOOrKamVlTwp8yZYqsa9eusoqKCr5DsXgikUjWuHFjmVQqlclk+v89M3V8/G/5ZGZmJntTlkne6o6ZlpamkMxFIpHsyJEjas8RHR0tCwgIUPimQMmeGIt1ZDUz05UImPdzc3NlQqGQfTBG0+c0vZ6QkMD+59a0ekObGzduyGrUqCHbv3+/Xp+zV0VFRTJnZ2fZhg0b+A6FEF5QwleDa8JnZm3Mk5b6JHxjPFXp5+cn69mzJ83u9bB69WqZi4sL+3ATIfaEbtpWQ/fu3SGTyeDn56fX5xITExEcHAyRSISSkhLIZDKUlJRAJBIhODhY4z4r8q5fv46YmBisXr0aNWrQXyNXixcvhkwmw1dffcV3KISYHWWKahg2bJhBnzPGU5Xr169Hjx498OGHHxoUg71q1KgRFi9ejG3btqGkpITvcAgxK1qHr4autdSa3uf6enUbb1y7dg29e/dGXFwcxo0bp/NYRJFUKsXrr7+OwMBArFu3ju9wCDEbSvhqWHrCnzBhArKzs3HlyhVqp2eg4OBghISE4Pbt23BxceE7HELMgko6Jib/EA0jICAAANj6vbpfmvzxxx/4+eefsXbtWkr21bBw4UI4Ojriv//9L9+hEGI2lPCNiNkZkXmqUiqVYseOHSrjqvNU5dq1a/Hmm2/Cx8fHmKHbnYYNG2LJkiXYvn07ioqK+A6HEPMw44ogqwGOyzKVqXsaU37DLHmGPFV59epVmYODg8aHdoh+SktLZU2aNJGtWLGC71AIMQtK+GoYmvBlsqqkLxQKdT59yYzV56nKsWPHyvr16yerrKzU84qIJps3b5Y5OTnJCgsL+Q6FEJOjm7ZW4sqVK+jXrx/EYjFGjx7Ndzg24+nTp3Bzc8Ps2bOxefNmvsMhxKQo4VsJHx8f5Ofn48KFC3yHYnO2bNmCDRs2ICcnB82aNeM7HEJMhm7aWoFLly7h6NGjWLt2Ld+h2KSPPvoIr732msJ2x2VlZdi1axeCg4N5jClQxdsAACAASURBVIwQ46IZvhUYM2YMHj9+jJSUFL5DsVkhISFYu3YtMjMzER8fj7Vr1+L+/fsAgPLycjg6OvIcISHVRwnfwqWmpmLgwIH49ddfMXLkSL7DsVmlpaXo0qULysvL8fjxY4XnIe7cuYP27dvzHCEh1UclHQu3du1avPXWW5TsTaS8vBy7d+9Gjx49kJ+fj8LCQlRWVio8/Hb79m0eIyTEeGryHQDR7Pz584iPj8eJEyf4DsUmJSYmYsyYMXjx4gUcHBxQWVmpMsbR0RG3bt3C0KFDzR8gIUZGCd+CrV27Fm+//TaGDx/Odyg2KTIyEs+fPwegee+imjVr0gyf2AxK+Bbq3LlzOH78OBISEvgOxWb9+OOPqKysRGxsLCoqKtSOefXqFXJycswcGSGmQTdtLdSIESPw8uVLnDlzhu9QbFpFRQXmzp2L3bt3qy3pAECfPn1w+fJlM0dGiPHRDN8C/f777zh58iTbKIWYjqOjIyIiIlC3bl2EhoaqTfpU0iG2gmb4FsjLywuVlZWU8M1IJpPh888/R0hIiNp6/tOnT1G/fn0eIiPEeGhZJo+++eYbuLq64tixY+xrycnJSExMpE5MZubg4IAtW7ZAJBKpfZ/q+MQWUMLn0ZkzZ3D37l2MHj0affv2xa+//oo1a9Zg2LBh8PT05Ds8u7R+/Xps2rRJ5fU7d+6YPxhCjIwSPo/y8vLY3//xxx/w9vaGRCKh3TB5tnz5cmzbto3tKFa7dm2a4RObQAmfR/I3A8vLywFUPeK/ZMkSDBo0CKdOneIrNLu3aNEi7Ny5Ew4ODigrK8OtW7f4DomQaqObtjx5+fIl6tWrp/WBn/LycnTo0AFZWVmoVauWmSO0bGVlZTh37hwuXryInJwcFBcXa1xWWR25ublITU0F8E9rSsJdnTp10LhxY/To0QPvvPMOunTpwndIdo0SPk8yMjLQtWtXneNq1aqFv//+GzVr0gpaALh8+TK2b9+OQ4cOobS0FO3bt0fHjh3h4uKCGjVM8wPrw4cPUVlZiTZt2pjk+LbsxYsXKCoqwvXr11FSUoLXX38dM2fOREBAAJo3b853eHaHEj5P4uPjMWrUKI3v16pVC7169cKpU6fQqFEjM0ZmmR4+fIglS5Zg37596NOnD+bOnQsfHx+0bNmS79AIR1evXkVsbCwiIyNRVlYGkUiEwMBA1K5dm+/Q7IfZmikSBd98842sVq1aahuZ16pVS+bh4SErLS3lO0yLsGvXLlmDBg1kbm5usgMHDvAdDqmmZ8+eyVauXCmrV6+erGvXrrI//viD75DsBt205cnt27fVliBq1qyJt99+G6dOnYKTkxMPkVmOiooKfPrpp5g/fz4+/fRT/Pnnnxg3bhzfYZFqeu211xAcHIwbN26gdevWGDx4MOLj4/kOyy5QwufJzZs3UVZWpvBazZo1MXz4cBw7dgyvvfYaT5FZhoqKCkyaNAlhYWGIi4vDunXrUK9ePb7DIkbUoUMH/Prrr5g6dSqEQiEiIiL4Dsnm0Z1AnmRlZSms0HF0dMTo0aMRExNDNU0ACxcuRHJyMs6cOYO+ffvyHQ4xkZo1a2Lnzp1wc3PDRx99BDc3NwwbNozvsGwWzfB5Iv/QlaOjIyZMmICff/6Zkj2A0NBQREREYP/+/WZJ9lKpFOHh4XBwcICDgwP27dun9vVVq1YhKyvL5PEYorqxMp/38fGBg4MDfHx8sG/fPkilUhNG/Y/PPvsMgYGBGD9+vMX+GdsEvm8i2KPHjx+zN2hr1Kgh+7//+z9ZeXk532FZhJycHFndunVlu3fvNts5Q0JCFG6ah4SEyGQymUwoFKq9qZ6Wlma22LiqbqwBAQFqPy8UCk0c+T/Ky8tlPj4+snfeeUdWWVlptvPaE0r4PLh06RL7H2rBggX0j1vOuHHjZBMmTDDrOZm/i/z8fPa16OhoGQBZWFgY+1pCQoIMgCwgIMCs8elS3VjT0tJkAGQikUiWm5srk8lkstzcXPabQGZmpsliV1ZYWChr0qSJbM+ePWY7pz0xyjr8wsJCnD59GhKJBA8fPsSTJ0+qe0ibdu/ePaSkpKBz587o3bu32c7r5OSEVq1aQSAQ4N1330XTpk3Ndm4uTp8+jTFjxiA9PR2urq5mOy+zZ478fwUfHx+IxWKUlJTA2dlZ61h9SSQSnDp1CkuWLDH4GPKqG2t4eDjmzp2LzMxMuLu7K8TZu3dvREdHw8/PzyixcrFz50588cUXSE9Pt/vFC8Zm8E3b8vJy7N+/H9+GhuF8ylnAoQYatnWHo1MzoE4DY8Zoc2SVjdCw8wAUOTdH4u3n5jvxy0eoeHIBpf/eAsgq4fHW25g/zx+TJk2yiCd5V6xYgXnz5vGS7OV/L5PJcOTIEZWxYrEYABAdHW3Quc6fP48ffvgBoaGhAMAmfPkYNNGWtKsbK3M/qUWLFgqvt2rVCgBw/fp1nccwprlz5+KLL75AaGgoPv30U7Oe29YZNMNPSkrC/I8+xs2sLDTu642mHhPQsMtg1KhVxxQxEiOrfPUSpRlnUZgSi6LL8ejs7o6d33yNoUOH8hbTtWvX0KtXL2RkZOCNN94w23nVJVvl/xJbt25FUFAQAOg925VKpUhOTkZ4eDjEYjECAgIwatQoDBw4kN1aoLoJv7qxavtJwBg/0Rhi/fr12Lt3LzIyMsx6XlunV8J/9uwZZs/xx/590Wgi8IKr33rUbd7BhOERU3tRcAd5+1bjsSQBk/wmIzIinJfOThs2bMDevXuRnp5u9nPrSmqJiYm4evUqzpw5A7FYzCmR5uXl4dy5c5g8eTKEQiGmTJmCt956y+Q/vRgSqyUmfGYCkJ6eThuuGRHnhH///n2MFvog6849dJixFY160lpZW1JyLRF3flgC9w5t8Yv4iNk3ChsxYgRcXV15efhGn6TG1LsTEhK0rhdnjmnu+rc8fWO1pIQPAI0aNcKWLVswd+5cs5/bVnFah5+dnY0+/frj9uPn6LriKCV7G9So5zB0XXEUtx8/R59+/ZGdnW3W81+/fh09evQw6zkNMXHiRADAl19+qXVcbm4uoqOjMXnyZHZNu/yzF/KYtfPafpkyVkvVtWtXXn7is2U6E75UKsVI71Eob+yGLp8fQp3GtEWsrarTuA26fH4I5Y3dMNJ7lNkeugGAoqIiNGnSxGznMxSzCoa5KaqJq6sr/Pz8UFJSAn9/f+zduxft27fH/PnzIRaLUVBQYDGxMn18lf++ma819fk1tWbNmuHx48e8nNtWaU345eXl8PlgHB6X1USnBZFwrGve1Tcvi+6b9XyWpOL5E+Qn70HG9plImd0GGdtnojD1MCqec1vyaujnHes2QKcFkXhcVhM+H4xjO3GZ2osXL+Do6GiWc3HBPHGqnASZRB0QEMDpOM7OzhAKhThy5AhSUlLYY8uviJFVPQ+j9ZcpY+3evTsAID8/X+F1po+vOVdNyXNyckJFRQUv57ZVWhP+N99+i4tX/0DHj76DYz3z7tz44PguXPlsgFnPaUly4zYi54elKJacBAAUS07i5q4FuBkeaPLPO9ZzQsePvsPFq3/gm2+/NfwirNiUKVMAADExMexrUqkUUVFRAAzrfjVo0CDs3LkTaWlpCAkJMU6gqH6sTCOeqKgotuyUl5eHuLg4AMCAAfb7/9DWaLxp++jRI7h16ozWU/+DJv3M31Q7ZXZV6cgj0v5m+c/u3sAfa4ej7ZhP0HzIVNRp3AYvi+7j/i87kJ8UhTc3/Ya6LdxM9nnG40u/4MGez5CTfRPNmjUz5iWqcHBwwJ49e9jkZU6abkwyDzQpE4lE2LBhg1li40qfWLU9aKYsICAAO3fuNHK03EydOhUAsGfPHl7Ob4s0zvBXrFyFuu168JLs7d3T21cBAM3e8mXvmdRp3AYthk6vej/3mkk/z2jSbzTqtuuBFStX6X8RNuDIkSOIjo6GUCgEUJX8EhISLC7ZA9WPNSIiAmFhYeznhUIhwsLC8MUXX5gsZmJ+ah+vzM3NxXeREei2/JBeB5Omn8XjS2LkJ0XBRTAcrYb7w7nrYIUxzMy93zYJHqX8jNyY9XARDEfTQePQdMBYhTHyv/eIvM/+vs9/UnH7p5Wo364b2o1byo4tTD2MwvMHUSw5qXJMdccrTD2Mm7sWAAA6z/sWLj2HsaWrjO0zUdulJdymKf6Dr3j+BKkfd0GLodNU3lM+hzbafnIpe1z1Xq2GirPq2s5VD+o8v5+p9djV/by8lmM/x3dfjINo5XK0b9+e8+esibYauZ+fH2/LKvXFNVZ119u8eXP4+/vD39/fFKERC6F2hh8REQGntu5wcuvD+UB3D27BjZCJyE+qqhsWS07iRshE3D24Re34W7uDkBuznh17c9cCFKYe5nSugjN7UCw5iXpt/nki8+7BLbi5a4FKzVrT+Zn3Gcr17VbD/ZGfFIVnd28ofO7vhzcBAC69vDjFaoh7R78CAJX7JrUaNlV431Sfl+fUsS+c2rojMjKS82cIIZZJ7Qw/7sAhOAlGcj6INP0s7h39Cm3HfILW78+HYz0nVDx/gge/7sS9o1+hcb8xqN+um8Jn6rfrhs7+O+BYzwnS9LO4ETIRhecPoumAsQqzeXUz4Xpt3lB4Xf788jXrgjN7cO/oV2jYZbDKTxr5Z/agz39SVcZK08/CuWvVeBfBcBRdOqoQ+5ObF6tiaNlR45+Hrd13cBKMROzPB7F+/Xq+QyGEVIPKDL+4uBgZN/5EQ/eBnA9SmnEWANhkD1TNLlu/Px8AIL3xm8pnWnrNYscyyZiZnevi3EUxeT++VHWziUn2QFXNuvmQqQrvy+swabXOsa2G++Pe0a8UlocyJSguNz1tRUP3gci48adZ1+UTQoxPZYZ/40ZVCaNea+4bWDElgtSP1e95kRuzHq1HzlN4jSkvGEL5s0wZSfmhMObr/KQolXq7csJWN5aZ5T++eBStR85jyzsuvYdrja+6NXxLU69V1Za5169fx1tvvcVzNIQQQ6nM8Jkn22rWb2T2YCxRiyFTkRuzHq9KC/H8f/X7Bq+/adJzth3zCQCoPCTFfM28b6rPK6vZwAVAVd8DQoj1UpnhP336FAD02uq4xdBpyE+KwoCvM8z+gJb8+V8W3VeY5b/Iz2HfV6ZprHIybOg+CAAgzTiLwvMHAUDlfoSy6s7emZvRr0ofKfx5vii8CwCo3UT7TxDV/bwy5t8C82/DluXl5fH2ZKklEYvF8PHx0WvTNKlUipiYGIjFYojFYnaXUG9vb4XGLIQ/Rmli3qRf1drdB7/uxKvSf2aB0vSzSJndBg+O7zL42Fy2EmDOX3BmD1tvf1l0H4/OVT0pqG5FjaaxDZXuDzjWc0Lned+yK4D0nR0bol6rzgCAR+fiFGIsunQUgO6fMKr7eXu1detWm116qg+JRAIfHx+9P7ds2TLMnTuXfYBLLBZj8uTJmDZNdcJF+GGUNkfOXQej7ZhPcO/oVypL/lwEw9HM40O9j+kiGI5iyUmda951nb/tmE/gIlBfc1feuqHtmE9UVvMAgIvc7qCNBO/pcxkGqd+uG1wEw9VeT4uh01R+wlBe0aTv50kVpnGIPTt//jw8PDz0/pxEIkFoaChEIhH8/f3h6uqKvLw8bN68GaGhocjKylJon0j4YZQZPgC0G7cUned9q1A+cZuxBR1nhhh0g7bduKXsscqK/+J8fia5uwiGo/O8bxUezFIe337ianZst6AYjWPlyyJ1m5rnx/2OM0PgNmOLwvW4zdiC9r4rzfJ5Yn+2bt0KDw8Pg1o4pqamAgCmTZvGlsRcXV3ZjduuXLlivECJwYzayLTpgLFoOmCs1tm4pvq28uv123WD27QvFI6lqzbOnJ+r1iPnqaweUkf+XkB1Vhfpo1bDpmjhORUtPKfqHKvuz0Wfz1uzxMRExMbGIjQ0FEKhEIsWLVJp9sHsHZOfn4+oqCgEBQWx9WXmyVRNvW2Z3+fm5uLjjz+GQCBQ2K5g37592Lt3r0LNWvlpV/nj7du3D5MnTwZQ1RxFvr7t4+ODNm3aqOxdI5VK0ahRI6372hijTWJQUBCOHDkCoVDIxsiVpfXFJeoZbYZvy5j6PrMXDbEMq1atgpeXF9sUXCwWw8vLC6tWqd/7Z86cOWzZhqkv79u3j9O5mJ60zFbCzPknT56sUrPWdH7mfYZyfXvRokUIDQ2FRCJR+BzTBGTUqFGcYjWUTCZj99LRV3BwMACo3Jxl+vYy7xN+UcLXImV2G6TMbsM+xUu1b8uRmJiI4OBgiEQilJSUQCaToaSkBCKRCMHBwSpJEwAEAgE7NiEhAQCwd+9eAIqzX3V70Hfv3h0ymYydvcufPzc3FzKZDLm5uez5ExMTVc4fHh6uMlYsFrNjhw0bBqFQyG5LzDh7turBRm3N3au7pz6xD5TwtWDq3+0nrtZY3yf8OH36NICqMgQzq3R2dmZn8KdOnVL5TGBgIDuWKfvo6gbFUC4TxcbGAgB7gxKoqlkzm48x78sLCQnROXbRokUIDg5WaIfIlKDopiepLpX98Pfu3YupU6da1ZOgxPRSZrcx6X71+u6Hr0/NWtN+98qvqxvH9bP6HlfbcXx8fDBkyBAsWbIEEokEvXv3RlhYmNadLI1Rw9cVl6HjDW2ETvvhGx/N8AmxMP7+/ggKCkJBQQFbv7f0rlOW2heXKKKEL4ep2Vu7YslJm7gObZjlfkxN3tw1a+b88qUXAMjKylJ4X56mscrJ0NPTE0DVfQLmHoNAINAaD981fEvti0sUUcK3Mc/u3kDG9pl8h2FyTJ/WkJAQtlk3UJUkHRwcsHXrVoOPzWVXUOb84eHhCn1gmT6y6lbUaBr77rvvKoxzdnZGdHQ0uwLIGmbH1BfXOhh1HT7h15OcK/hzo2HL6qzNsGHD2BUxykv+hEKhQY/zC4VCiMVinWvedZ1fJBJpXN6ovHWDSCRSuSEMAN7e3uzvR4+2vDajynV5gUAAoVCo9s8jICBA508oxDxohm8jHhzfhT83CtF53rd8h2I2GzZsQHR0tEL5JCwsDBEREez6b32Pxxzr/n3dixaY88v3gY2OjtbYR3bDhg0ICQlhx2rrOSu/nt3NzTp6L1BfXMtnklU68r1tgao9atR1vXp29wakN35jWx2q60Mrv09MseQkMrbPhItgOFoMmcoum1TuTavp89p62CqP1XQ9mnr16nPdyoyxf37K7DbosnA3XATDtXYLM5SlrdKxJoasUsnKysIbb7yh8ycNW0ardIzP6CUdJinLYzbx6hYUo9DdSnlcseQk2/VKeYsE+fHMuF5rT6Lo0lGFDcKYhK7u88o9bF0Ew9Fl4W6t13P34BaF4zPnbjvmE4W1+Vyv21RoGa1tYer76m7+EmIooyd8Jukx/WKBf2rLjy+J2cTHjOuxUsw2S39ZdB9XPhuAm7sWqCTspzlX2f32mR64f6wdjrZjPlF5Xd3ndfWwVUefXr1cr1sdStaEIb+eXiQSUe2bGJXRa/hMmeXxxaOQpp9FxfMncHLrA4/I+yoboXlE3kfdpq54dvcGiiUnUXBG849u6nrgAop9dLUlVS49bJXp06uX63UTog1T/w4JCdFY3yfEUEaf4bcbtxTFkpMKdXlNNW/lcok2mnap5Nphi0sPW2X69OrV57qV2VoPXKJIn9r9kSNHTBgJsXdGT/j123WDR+R9hRuyxZKTcBEMR7txS9kSSH5yVUmlxdBpaNJPiJoNXFDbuTkuLbbOH2G5XjchhPDFZOvw67frhvrtuqFJ/zF4kX8HN0Imolhykp2p5vxQdcNTfnbNpZ2hobj2sJVnSK9eXdetDs3e7Zuhe81YAqlUivj4eIWeAEKhEGPHjjVoaSwxLaPX8HOiliFldhs8yanqcFOncRvUbdFB43gm8TI3Q02Faw9befr06tX3ugmxdlKpFNOmTVPpCTB37lzMmTNH4QloYhmMPsNvNngi8pOi1D7x6TZjC/t7pjH41RXvqD3Oi/wclbp7dXHtYcvQp1cv1+smxFbEx8dDLBYjLCwMEydOhLOzM6RSKUJCQhAcHIyoqCgsWbKE7zCJHKPP8J3c+qDX2pMKpZK2Yz5Bl4W7FdrtNR0wViERth3zCd7c9Bt6ra1ahy/NTDFqXPr0sFX+HJdevVyvmxBbwWzs5u/vr7YnATWFtzw2vx++KZ46tUfW8KStfH9boGodu6+vr8padolEglOnTrEJSV0vWvm6ulgsho+PD4RCIfz9/dmlk8r9aTV9XlsfW+Wxmq5HU79efa5bmbH30Fd37Orcl6AnbY2P9tIhNoHpZ8skPaCqj2rv3r0V2g2KxWL07t1bYfaprb8tk+zlfy+RSNh+tgxtn9fWx1YTrv16uV63OTHbPkdHR/NyfqIZJXxiE5ikzPSMlclkSEmpKgvKtxBkxqWkpLDjcnNzAUAhMTNSU1NV+uD27t0bAFReV/d5XX1s1dGnXy/X61bHVHvoR0VFQSgUKuz4SSwDJXxiE5gyS2xsLBITEyGVSjFo0CDIZDKFzceYRObm5gaJRAKxWIzw8HCNx1XXBxdQ7KWrrszC4NLHVpk+/Xq5Xre5rFq1CsHBwdiwYYNC2YpYBpuv4RPjsPQaPtP7laGt5s0kJXX07YOr6fXq9LzVp7auz3VrioPLebhg/lzT0tKMsgcQ1fCNj2b4xCYIBALIZDKkpaUhJCSErW0zNXdGeHg4goODERAQgISEBKSlpam05bMmXK/blAoKCrBq1SpIJBJkZmbShm8WzGI7Xlnr6hrlfXGY+CueP0HhxSMoTjvJbrnQdNA4lT35DcVsz6z856UpHlslEAggEAgwYcIEZGdnw8vLC2KxmJ2pzp07FwAUyh1cWhoaKi8vT6Gfq6Y+tvICAgIQGhqKkpISzmURXdetjjGe7GVuYAsEAoMbzxDzoRm+meTGbUTOD0vZ/f6Z/flvhgdW+9j20sdWm/nz58PBwQHnz58HUFUv79Spk8bxTOJlHhQyFa59bOXp069X3+s2pry8PPTu3RsCgQAbNmygZG8FLHaGb+3kZ9LP7t5AflIU2o75BM2HTGX35L//yw7kJ0VV66liXX1smTi47MhpzWbMmIHQ0FB4eHiovBcWFsb+nmkO/sYbb6g9TlZWFtzd3Y0aG9c+tgx9+vVyvW5TOH78OACojZNhjfsD2TKa4ZvB09tXAQDN3vJV2JO/xdDpVe/nXjPouPbYx1aTQYMGIS0tTaFUIhKJcOTIEXZlDAD4+fkpJEKRSITMzEykpaUBAM6cOWPUuPTpY6v8OS79erletykw5TFiPYw2w0+Z3QYthk5Tu7d8TtQyhV0nufSyVXd8QLUGXd0+tJrOo42+dfCyx1XjazVspvB6beeq/7jP72fqdTxGbsx6to+tfPtGe8XUsXUlVH9/f7XJUH42qmlmqu/rALBkyRKte8po+qyfnx/8/Px0Lq/ket3GRrN362O0hN9+4mrkxqxHu7FBCnvMvCotRH5SFNpPXA3Hek5697I1BNc+tObCxKJ8c5b5c7p39CuD4rL1G7CEEOMyWsJ37la166U046xC0pb+r01g495VLQD17WWrL3360KpDSZQQYquMVsOv364bXATDUXj+oMLrhecPosXQaexNSX172epLnz60hBBiT4y6SqfVcH/cCJnIrjp5kZ+DYslJdAuKURinTy9bfenTh1Yd6i9LjIHq28QSGXWVToMOvQD8s5c9s/qEeR1Q7GXbLSgGvdaeRL9t5nkikC/MHvnKLRyZr7W1WSSEEGMx6gzfsZ4T3GZsQc4PS9G490jc3LUAbjO2KNysNGYvW/m2gwxD+tDKM8XsvV6bqjXfr0ofKcT0ovAuAKB2E9teI28trLW3rPKeOOriZ7Z2Nua1aToml3gIP4y+Dt/5jaoHQC4trtpPo1GPoWrH6dvL1kVQddOX6Rlb8fwJ/kr4TmWcPn1ozaVeq84AgEfn4hT66hZdOgoAaPD6m2aPidgPiUTCbqNsycckpmf0J23rtnBjZ9kthk5jHzRiGNrLtumgcSiWnFR4qpRpWShPnz605sLc0FYXU4uh0xRWDVnrHkKEf+pm0ufPn1f7FG516DqmPrt+EvMyyZO2zCy72eCJKu8Z2su26YCx6DzvW3am7zZji8abr1z70JpTx5khcJuxhY3fRTAcbjO2oL3vSl7iIbZv69at8PDwMGrnKVMck5gP7YdvZMaYoafMbmP0P//qxmWJ++E7ODggICBA7ZOo8+fPV9hxUt8+tuq+1jSOwbUHrbrr0EVbHVxbnEeOHIFQKDTa/Ql9jlndc9J++MZHm6dZmCc5VxR+AiKahYSEICgoCOvWrVPYX6agoAChoaEICQmBs7OzQl9ahlgshlgsBgCFpG8o5aYqzPFFIpHZtzxgmOJmKd2AtW60eZqJpMxuY9AOlU9uXkQLz6m8x2EN3nvvPQBQ6Q/LfM20/9O3j62+9OlBq46pessSoowSvoXR9lAYUSQQCCAUCrF3716F1/fu3YuAgAB2m2N9+9jqS58etITwiUo6RmZp9z4sLR5jW7RoEby8vNh97LOysiAWi5GQkKAwTlsf2+pijtuoUSO17wcFBWndLdPYvWUJ0YRm+MSq9e3bF8A/+9hfuXJF4XXA9vrYEmIoq0v4tlyTVlYsOWk312ooZ2dnhIWFYe7cuSgoKMDkyZMRFham0AtWvo/tsGHDIBAIUKdOHYPOJ99ykME0KWHq9/rW4KmGT8zF6hK+vaA+tdwNGTIEANCiRQsAwMiRI9WO07ePLXPTl+kXK5VKsWPHDpVx+vSgJYRPlPAt0JOcK/hj7XC+w7Aa7u7u7Cw7ICAArq6uCu8zDwm98cYbcHBwQKNGjRTq+cw3AmXMMwEeHh7s59TV6eV70LZo0QIODg5wcHCAl5eXSg9aS8TES2wfJXwLQ31qDcPMsmfMmKHynqF9bP38/BAdHc3O9MPCwjTefOXah4GTmAAAIABJREFUg5YQPlnUKp2K509QfC0RhecPolhyEi2GTkPrEXPV7q0jj2uPXPk+t0DVtg7qOmBxHafMGHvpU59awwwbNkxrrdvQPrZMX1lNn1E3VlcPWj5o+7ORyWQGzfDp3oL1saiEfzM8kO1tCwD5SVHIT4pCr7UnNSZbrj1y1Y1jNjPrFhTDNjjnOs5UbH0ZJbE858+fV/gJiNgui0n48o3GmfaEhamHcXPXAuQn/aiwf748rj1ymXF9/pPK7uD5JOcK/twoxONLYjaRcx2nDiVrwidD9645e/as1ucEDI2DWB7LSfh/VD0o09JrFtskpOmAsTqbmjNJ9lVpIZ7dvYGyovt4mnNVZZyLYDiKJSfx+OJR1HftgQYdesHJrY9KkuY6jhBbYcxkTyybSsKvUYOf+7hMvdyQ7Yu59MhtN24piiUnFer8rYb7q8zYuY5Tx9b74fL1b4NoZ2m1dEuLh/xDJeEzD6xUvHgKx7oNzB6QvuR75DbpJ0TNBi6o7dyc7bjFqN+uGzwi7yvc4C2WnISLYDjajVvK3iPgOs6eVLx4CkDz1gGEEOugkvBbt24NACgryUe9luZL+EyXrFelhXrN8vXtkVu/XTfUb9cNTfqPwYv8O7gRMhHFkpMqM2+u4+RZ8+xdm7LivwD882/DHllrv1uupFIpYmJi2G2dmX4B3t7eCk8tE+um8jN6165dUat2Hfx994ZZA2n4v164fyV8xybswtTDSJndBjlRy3R+XleP3JyoZUiZ3YbtiVuncRvUbdHB4HH25O976ahVuw66du3KdyjERJYtW4a5c+eyPQLEYjEmT55s8Q+NEf2ozPBr166Ndzw9cf1GMpr0F6r7jEk0HTAWhecPauj7Ol3j57j2yG02eCLyk6IUeuIy5BuOcB1nT6Q3kvGOpydq1arFdyjEBCQSCUJDQyESieDv7w9XV1fk5eVh8+bNCA0NZXciJdZP7V24KX6TUCo5jsryMrMG09l/h9p+t9rq5lx75Dq59UGvtVXLPuXHdlm4W6HhCNdx9qKyvAylkuOYOrn6XaGIZUpNTQUATJs2jd2WwtXVlX1qmNmBlFg/tcsyJ0+ejKDPl6Hw/EE0f3uS2YJxrOeEFp5TtSZWdXVyTZ/RVJdvN26p1ji4jjM1S7gnUHj+IOrUdDBKG0BLJZVKER8fj71790IsFiMgIACLFy/WOavl0icXUOx1C1Rt7eDr6wuBQGDQOGXV3U8/Ly8PwD+bzzFatWoFALh+/brO4xProNLEnBEaGorPROvRbcMZONapb+64iAWoePkMN1YNwX+CVyvsEWMKhjQxNxYfHx+2di0vLS2NTbbKN23V9cllREdHs0lf27iEhAS2yTnXceqYqgm6rvdMjZqYG5/GhdX+/v5wbd0cD45+ac54iAV5cPRLuLZurnYPGlsh32yc2c+e2V2TmWmrw7VPLjMuNzeXHZeSUlVmjI2N1XucOrSfPuFK45O2jo6OCP1mB4YMHYr6r7+Jxn1GmTMuwrOiK8dw/9dQ7E1KgqOjI9/hmMyxY8cAAIGBgezyQ3UbpiljkmhBQQEkEgny8vLYWrg8oVAIsViM2NhYvPnmm+jbty8GDRqkkoS5jiOkOjSWdBibv/gCa9atR5clsWjg9qa54iI8eppzFRlbJ2DdmtVYvkz3klhj4Kukw7VkoW6ctj65zDiJRILevXuzrwuFQixatEilRMN1nLbYtLHGko6fnx8cHR2ppGNEOp+VX75sGXw//BDZX8/Ak5uqMxhiW57cTEX21zPg++GHZkv2AODk5IQXL16Y7XzVxbVPrkAggEwmQ1paGkJCQiAWi+Hl5QUfHx9IJBK9x5mCSCQCUHXzWh7zNfO+uZWUlKBu3bq8nNtWcdocZfd3kfB+712kb/XDo5SfTR0T4cmjlJ+RvtUP3u+9i93fRZr13K1bt8aDBw/Mek7gn3606nrVaqNvn1yBQIAlS5YgNzcXCQkJEIvFCjN6fcfJq24Nv3v37gCg8g3rzp07AKDSQcxcHjx4wK4UIsbBKeHXrl0bcbExWLFsKW59twg533+KMql+/0GI5SqTFiDn+09x67tFWLFsKeJiY1C7dm2zxiAQCNgOVObE9MPdsWMHO6Pdt28fHBwcMH/+fJ2f19Und/78+XBwcGD74rq6uqJTp04GjzMF5gnqqKgodolmXl4e4uLiAAADBgwwSxzyXr58iYyMDJ1LUol+dNbwlR04cACBnyxGYVExWo1ZhBZDp9GyTStV8fIZ8pOi8PDol2ja2AU7vtqG8ePH8xJLREQEPv/8cxQUFJj9JrEhyzL37dunsBpHWWZmJtzd3XH+/Hl4eHioHRMWFsaugOI6zlQ0/RkEBATw0sHrxIkTGDVqFAoKCtC4cWOzn99W6Z3wAeD58+fYtGkTQrb+F5UOjmjUZxScuw9BfdceqO3cnN3PnliWiudPUCYtwLO8PyH9MwklV+NRQ1aBoCWfYsWKFahXrx5vsRUWFqJt27Y4dOgQ3n//fbOem9k4jCnTiEQiTJs2TeHBK3U3L8PDw1U+8/z5c/Tu3VshSUskEsTFxbE3eEUiEQYMGMD2ymVwHWcKBQUFOHz4sMLmaUKhEBMnTuRl87RZs2bh4cOHiI+PN/u5bZlBCZ9RUlKCPXv2IO7AQfz+228of2XerRiIYWrWqo2333kHvuPHYerUqRaz7fGsWbOQn5+PX375he9QCI8ePXqE9u3bIzY2FqNHj+Y7HJtSrYQvr6ysDOnp6Xj48CGePNG8PTHhj5OTE1q1aoWuXbuavUbPxf379+Hu7o6DBw9ixIgRfIdDeOLv74/bt2/j1KlTfIdic4yW8Akxhg0bNmDv3r24cuUKryUmwo/U1FS88847uHz5Mnr06MF3ODaHEj6xKC9evED37t3h4eGBqKgoaohtR+7duwcPDw/4+vpi27ZtfIdjkyjhE4tz7do1DBo0CJ999hnWrl3LdzjEDP7++2/2qeKkpCR64MpENO6lQwhfevbsiejoaHz44YdwdHSESCSimb4NKyoqwvjx45Gfn4/U1FRK9ibE6cErQszNx8cHkZGRCA4OxvTp0/Hy5Uu+QyImcPPmTQwaNAh3797FsWPH0KxZM75DsmmU8InFmj59Ok6cOIFjx45h0KBB+O233/gOiRhJeXk5vv76a/Tv3x8tWrTAhQsXqGeyGVDCJxZtyJAhSE1NRcuWLTFkyBBMmTIF2dnZfIdFDCSTyRAfH8/uGRQQEIBTp06hadOmfIdmF+imLbEaBw8exOeff45bt27h3XffxdixYzFw4EB07NgRLi4uqFGD5i+W5sWLF3j8+DFu3LiB06dPIy4uDjdv3oSPjw+2bt1qtv2CSBVK+MSqVFZW4tixY4iJicGvv/6KR48e8R0S4ahHjx4YNWoUZs6cSeUbnlDCJ1btzp07yMnJQXFxMSorK4123OfPn2PhwoWYNGkS3nvvPaMd11Jt2LABLi4u+Pjjj4163Dp16qBJkybo3r27xWzhYc8o4ROixpo1a7Bjxw5kZ2fbxW6N+/fvx5QpU3D16lX06tWL73CIiVDCJ0RJfn4+OnbsiFWrVuHzzz/nOxyzkMlkGDBgAJo3b06b19kwSviEKFmwYAHEYjGysrLsaj+fhIQEvPfeezh9+jSGDh3KdzjEBCjhEyLn5s2b6N69O3bu3InZs2fzHY7ZjRw5ElKpFCkpKfR0sw2ihE+InIkTJ+L69ev4448/zN55yxJcvXoV/fr1w/79++Hr68t3OMTIKOET8j+XLl3CgAEDcPDgQYwdO5bvcHgzdepUXLp0CdevX0fNmrTdli2hhE/I/3h5eeHFixc4e/Ys36Hw6vbt2+jSpQu++uorBAQE8B0OMSJK+IQAOH78ON5//3389ttvePvtt/kOh3eLFi1CTEwMsrOz8dprr/EdDjESSvjE7lVWVqJv375wdXXF4cOH+Q7HIhQUFKBz585YunQpVq5cyXc4xEho8xFi96Kjo3Ht2jVs3ryZ71AsRvPmzbFkyRJs2bIFhYWFfIdDjIRm+MSuvXz5El27dsW7776LyMhIvsOxKM+ePUPHjh0xefJkajloI2iGT+xaaGgo/vrrL2qlqEb9+vWxZs0a7Ny5E3fu3OE7HGIENMMndqu0tBQdO3bErFmz8O9//5vvcCzSq1ev0L17dwwcOBBRUVF8h0OqiWb4xG6FhISgoqICy5Yt4zsUi1WrVi1s3LgRe/fuhUQi4TscUk00wyd26a+//kKnTp2wevVqLF26lO9wLJpMJsPAgQPRpEkTxMfH8x0OqQZK+MQuLViwAEePHkVmZqZdbZBmqMTERHh5eSExMRHvvvsu3+EQA1HCJ3bn5s2b6NatG3bt2oVZs2bxHY7VeP/991FcXIzz58/TxmpWihI+sTsTJkzAjRs37HaDNEOlpaWhb9++tLGaFaOET+xKamoqBg0ahEOHDsHHx4fvcKzOv/71L6SmpuLGjRu0sZoVooRP7MqwYcPw6tUr/Pbbb3yHYpVu376Nrl27Ytu2bZg/fz7f4RA9UcIndiM+Ph6jRo3C2bNn8dZbb/EdjtVatGgR9u/fj+zsbNSvX5/vcIgeKOETu1BZWYk+ffqgQ4cOOHToEN/hWLXCwkJ07NgRQUFBWLVqFd/hED3Qg1fELuzduxd//vknNm3axHcoVq9p06b47LPPEBISQhurWRma4ROb9/LlS3Tp0gVeXl6IiIjgOxyb8OzZM3Tq1AmTJk3Cl19+yXc4hCOa4RObt3PnTuTn59MGaUZUv359rF69GqGhobh9+zbf4RCOaIZPbBqzQdrs2bPxxRdf8B2OTSkvL0e3bt0wYMAA/PTTT3yHQzigGT6xaVu2bEFlZSU+//xzvkOxOTVr1sSmTZsQHR2NtLQ0vsMhHNAMn9ishw8folOnTli3bh2CgoL4DscmyWQyDBo0CC4uLvj111/5DofoQAmf2KyAgAAcO3YMWVlZqFu3Lt/h2KzTp09j2LBhSEhIwLBhw/gOh2hBCZ/YpKysLHTv3h3h4eGYOXMm3+HYvFGjRuHRo0dITU2ljdUsGCV8YpMmTJiA9PR0SCQS2iDNDCQSCfr06YPo6GhMnDiR73CIBpTwiVVLT09Ho0aN0KpVK/a1CxcuwMPDA4cPH4ZQKOQxOvsybdo0XLhwAdevX0etWrXY169cuYL27dujSZMmPEZHAFqlQ6xct27d0Lp1a6xYsQIlJSUAgGXLlmHw4MGU7M1sw4YNyMvLYx9uu3btGt5//3307dsXK1eu5Dk6AtAMn1gxqVSKRo0aAajqvfraa6/B19cXkZGROHfuHDw8PHiO0P4sXrwYP/30E9577z3s378fNWvWxKtXr+Dl5YVTp07xHZ7doxk+sVrp6ens71+9egWpVIrvv/8eTk5OyMrKQmVlJY/R2Z/CwkKUlZWhqKgIP//8M2QyGV69egUAuH79Os/REYASPrFi6enpqFFD8Z9wZWUlnj59+v/tnX9cVHW6xz8ICAgDDgIDDiighEsKaIISry5mjVaiXjdjK5Zk9arpTdnt1aK7df11+7Xoq1a917KyqAm7IuU1wbLZulBLKIiK6ChhKAHG4MA4DMQoDnP/GM9pzswZ5swwv/m+/4Jznu/5fg/iwzPP9/k+H6xcuRJJSUmoqKhw0upGDwMDA3j11VcRFxeHd999F0NDQ7Sjp5DJZOjr63PSCgkUxOET3JampiZW1SWtVouhoSFcuXIF2dnZpIeOnQkJCcGLL76Ivr4+I0dPodVqcfnyZQevjGAIcfgEt+XChQsmHQwAuh5cJBI5akmjkmeeecaszZgxY9DU1OSA1RCGgzh8gtvS2NgIUzUHvr6+GD9+PBoaGpCZmenglY0u3nvvPezZs2fYA1e+vr6QSqUOXBWBDeLwCW7J7du30dHRwXrP19cXEydOxKlTp5CcnOzglY1ONmzYgIMHD8LHx8doXwXQbaoTh+98iMMnuCU//PADNBqN0XVfX19MmzYNp06dQnx8vBNWNnp58sknUVFRAT8/P6PTzUNDQzh//ryTVkagIA6f4JZcunTJKIXg4+ODuXPn4p///CcEAoGTVja6WbBgAaqqqhAUFGS0od7a2oo7d+44aWUEgDh8gpvS1NTEOL7v7e2N7OxsSCQSBAcHO3FlhLS0NJw8eRIRERGMfyONRoMrV644cWUE4vAJbolUKqVTOl5eXli5ciXKysrg5+fn5JURAGDatGmora1FbGwsw+mT0kznQhw+wS05f/487fBfeuklvPPOO6QrposhFApx8uRJpKam0ukd/dPRBMdDeukQ3A6tVktXguzZswcbNmxw8ooIw/HLL7/g8ccfx5dffompU6eiubnZ2UsatRCH70ROnz6NL774At9WVeLihUYobiqhvnXb2csiGDBmjBfGhwQjPi4O6XPvxyOPPAKRSOSWKlrXr19HeXk5JJJ/oP7sOchknfilT+XsZRFYGBfEg0AQiftmpkIkehiLFy9mtAG3BuLwHYxWq8Unn3yC1155GReklxAXzsP9k8YhITwA/HE+8PchWTZXQzOkRa9ag2sKNc5eV6P+p5sI5gVhzbPrsWnTJrpjpytz7tw5bNm6DRUV5fAN4IGXeD/GTZoB35BweAfwnL08AguaARVuK7sw8NMFqJqqMTjQh0WLsrFj+zakpqZa9Uzi8B1IfX09nlv/LGrr6rE8NRyr5kRielSgs5dFsBB5/yAOne3C2zVd8PYbh9eLdiI/P5/1wJGzkcvl2LJ1K97Z/w54ccmIWPAsQlMXwsvbuAcRwXXRau6g59wJdH31NlRXz2PN2jXYsX07wsLCLHoOcfgOYufOndi8eRPSJ4/H9oUxxNF7AL3qO9j5f+34qE6G+Q8+iNKyTxESEuLsZdHU1tZi0eKl6B/UYuK/bkJEZg5A9GbdG60WXdWluP6/f0Ogrxcqjh1Feno65+HE4duZwcFBrF/3LIqLi7H9kclYkRZJ/s95GBc7+7Hy0I8IjohG+fEvEBcX5+wl4fDhw8h7ZgWC752HuJV/h7d/kLOXRLAhGnUfrr7/R/RerIT4ow/xxBNPcBpHHL4d0Wq1eOLx30Jy4jjeXj4FWVNcP9dLsI6uvkGsPHQFXYP+OFlbh+joaKetpaSkBHl5eZi48FlMWv5XwMv1Uk0EG6Adwk9lr+L6ibchFouRm5trdghx+HbkL3/ZjH17/o5PVyQiKZKkcDwd9eAQcg82oz8gEidr6zBu3DiHr+HUqVP4l6x5iMz+I4SPkXLV0UDH8b3oLP87vq2qxJw5c4a1JQ7fThw+fBhPPfkkip++B/MT+A6fv0N5C8KQ0XnqVKXW4POLckiaFJA0KSBK5GPZjDDMT+CD52/+cNZIxnf3D2LpB02Y9cBDKPv0iK1eiROdnZ1Imp4M/xmPIDbvdYfODQC3ejrgFyp0+LyugGZABXnd51Cck0DRIAE/RYSwucvAnzGfUxXUSMdfE2+GuvFLSC+cR2RkpEk74vDtQG9vL+6ZGo919/GwOmNkdbPWsP/769hxohUd20eniPfm8haI62RG10WJfBQ/Pc3u46/IB/DYu1IcKvsMixYt4rZoG/D7Z/LxRd1l3POnQ/Dy8TU/wIZcP7EfraU7kHGAvWW1p9Mi3gxZpdjoOj9FhGkbi+0+XntnED+8+Ts8mjYNH39k2p7UZtmBl/9zB6ICtFg11/HOHgB2nGh1yryugLSzH+I6GQqyopF7XwSEIX7oUN7C3u86IK6ToaVbjfgJpg9MjXQ8AEwNC8DGB6LwfMEGLFiwgNFLxl6cOnUKh/7nE0zfKnG4sweA1tIdDp/TVehvk0JWKUZ0dgEisnLhFyrErZ4OdFTshaxSDLWsBf4C0626RzoeALx8fDHp90U4tF2E59Y/i7lz57Lakd0cG3PlyhXs2b0H/7lwIsaQahyHc7ZDJ5S9PCWcTmkJQ/zwzGxdu+TG68MLaY90PMWauZEYVHVj7969lr+EhWi1WqzfUIDIh/6AgKipdp+PwKTv6lkAQPj9y+mUll+oEIJ5OunHvtZGu46nCIiaisiH/oB/3/hHk0pwJMK3MW++8QZmTQrGrGjLTi9WX1Xi2MVuiOtkECXysTojCplxzJpu4dYaAEBD4Wx82nADO0600vnlpTPCGDb6X3dsz6C/rn1+Fl6suIqkyEAUzo+hbY82ynGkUc7IWVPPZHve0UY51pfpeqLsW57AyG/nH7yMyOCxeD2bGZWo1BpMe60WeWkCo3uGcwzHcKmqDqWuNUV4IDPKjeCNBQA03RgY/tkjHE8x1mcM/i1tAnYVvY6NGzeyiq3biqqqKpw7U4+ZRW9ZNE55qRrdp49BVikGP0WEKNFqhPyGKQdZs0rngGa/2YAbNZ+itXQHnV8OS1/KsNH/OuNAB/31rJ21uPrxiwiMSULMskLaVl57FPKTRxg5a+qZbM+T1x5F8/71AICEtfsY+e3Le/Ixlh+JeIO9C82ACrXPTYNgXp7RPcM5hmO4VNXtbt093+BwxvWxIREAgIGO4bV8RzpeH4FoLc4WpuPbb79FVlaW0X0S4duQwcFBlHwsRs4MyzZpi75pQ06xlM4bS5oUyCmWouibNlb7F47+SKdtJE0KrC9rxtFGOae5Suq7IGlSIDE8gDH/+rJmSJoUjGeamp+6T7G+rBkbPvv1+9UZURDXySDt7GeMa5b/AgB4yI6b2Lur2gHAaHM17K4Dp+7ba7w+v00Og1zeg6+++orzGGv4oPhDhE7Pwtjx3EVf2o4UQborh84bKxokkO7KQduRIlb7H4tfoNM2igYJmvevh7z2KKe5uqpKoGiQIECYyJi/ef96KBokjGeamp+6T9G8fz2a3/21CilKtBqySjH625gyir/8rPu95Cc/xGmt1tBevhsAjDZXfYPDGPftNV6fseMFCJ2ehfc/KGa9TyJ8G1JdXQ2lqg/z7+Feb199VYndVe0oyIrGuvsngufvDZVag7e+v47dVe3ITgo1KulMigzE3t8mgOfvjeqrSuQUS3GkUY6lM8IY0TxbJJwYHsC4rj+/fs66pL4Lu6vakRkXbPRJo6RehtrnZxnZVl9VIjMuBJlxIRAl8lEu7WGsve4nXZOuKWEBMIUnbTQH+/tgdmwIKioq8Nhjj9ltnvKKCoQ88ifO9spL1Wgv343o7AJMfGQdvAN40AyocP3Lt9Bevhuhs7MRGJPEGBMYk4SE1XvhHcCD8lI1pLtyID95BGHpSxnRPFskHCBMZFzXn18/Z91VVYL28t0InpZp9ElDVlWCWTtrjWyVl6oR8hudPT9FhJ7T5Yy1q5rrdGuInGLy5+FpG828GQ+hvOJN1nskwrchdXV1iA4NoqNBLlRf7QUA2tkDuuhy3f0TAQDftSiNxqycE0nbUs6Yis7NkRnPdN7HLnYDAO3sAV3OOve+CMZ9fbYsjDVruzojCrur2tGhvEVfo1JQ5jY9PYkZAj/UnTKfprKWq1evokd+A0GxKZzH9F6uBgDa2QO66HLiI+sAAErpd0ZjIh9aSdtSzpiKzs0RMo3pvLtPHwMA2tkDupx1RFYu474+sb/bYtY2SrQa7eW7cavnVwdOpaDMbXp6EkGxKeiR30Brq3HxBonwbciPP/6IuFDLat+pFMG012pZ7+840Yq1d50/hSV/UAwxHEulkQxr9qnvxXUyo3y7ocNms6Wj/IvdWHv/RDq9I0ocPp0z0hy+qxHL90fZ9y12e/6PP/4IAPCPiOU8hkoR1D7HXmLaWroDExeuZVyj0gvWYDiWSiMZ1uxT38sqxUb5dkOHzWZLRfnddeWYuHAtnd7hp4qGXd9Ic/iuhn/4ZAC6ApLJkycz7pEI34aoVCrc3dsjAMi9T4AdJ1oh7x9E893NzplC+/Z0KcjStTRQqTWM69T31H17jTck2N8Hyl779ZtXKnWfAEmLYx2CrFy0lu7AYK8cA3fz90FxM+06Z3R2AQDdBrE+1PfUfXuNN8R7nE7T+ebNm0b3SIRvY/x8LKvFzEsTQFwnw+W/pHM6BWprqPkNT+a2dKvp+4aYsjV0hnMn637xqluUOHJ3U9lci4mRRu/UZvSN/kHGz7Ptpm6NwpDh/yKPdDwbGs2QxWO4cuuWLmXmNYb7745gXh5klWKk/9dlp/yhoOY3PJmrlrXQ9w0xZWvoDIPv0dWfKy9XQ35Sd9LZcD/CkJFG79Rm9GDvDcbPUy3XFT2MnTD8J4iRjjeE+l2gfjf0IRG+k1l87wQAwFvfX4e8f5C+Xn1VCeHWGuz//rrVzzaMUoebv6S+i863dyhvoazhBgD2ihpTtplxwQw7nr839i1PoCuALI2OrSHhrsMua7jBWGO5tAeA+U8YIx3vDkyYvRgAcP3LtzDY+2t1l/JSNWpWCXH9xH6rn20YpQ43f1dVCZ1vv9XTgRvflwFgr6gxZRtssD/gHcBDwtp9dAWQpdGxNQREJQAAbnxfxlhjz+lyAOY/YYx0vCWQCN/JZMaFoCArGrur2o1K/kSJfDyeEm5ipGlEiXxImhRma97NzV+QFW0y557+xhkjW8NqHgCMPkIPW1C9ZC1JkYEQJfJZ3ycvTWD0CcOwosnS8e5IyG8yEZ1dgPby3UYlf/wUEcIzHrf4mfwUERQNErM17+bmj84uAD+FPed+5s/pRraG1TwAwJ8xn/56fMrDlryGVQTGJIGfImJ9H8G8PKNPGIYVTZaOHwnE4bsAhfNjkBgegJrWXnoTtWhJPBZOC7Vqg7Zwfgwig8dCXCdDZ695jVxqfnMHr/TtQ/y96aobtkNiFPppkUl8x1Tn7Fo6BScu9zCan4kS+VhyL7eNx5GOdwdilhUiQJiI3qYaehM1fkURQlMXWrVBG7OsEGP5kZBVinFb0cl5fnMHr/TtvceF0FU3bIfEKPTTIv5hkyx+F2uYkr8LPedOMJqf8VNFCEtb4pDxXCHN02xIbm4uBi58hf96PMHZS7HosJWqAAAU8ElEQVQLw9X3m6KlW40H9pw1+0nDUzlyXo7nPm02edR9pBw8eBC5ubluVUViCcPV95tCLWvB2b8+YPaThidTs0qIkpISPP3004zrJIdPsCtUfp/qRUMg2Bsqv0/1oiH8CknpEOyCfj19QVa0R+S+Ca6Nfj19dHaBTXPfngKJ8Al2gdrs3bJwMqNJG4FgL6jN3sk5WxhN2gi/QiJ8Amcsyd1zEQohEMxhSe6ei1DIaIdE+AQCgTBKIA6fYBOEW2s49cFxdSRNCo94j9FAzSohpz44ro6iQeKw9yAOn0C4i7SzH/kHLzt7GYRRRH+bFJf35DtsPuLwCQQAZ9pVEL113tnLIIwiVC1ncH7b8J08bQ1x+IRRz/7vr2Pxuxewb7lnHpgjuB7XT+zHhVcWI2HtPofOS6p0XAx9bVtAV8POpnol7ezHdy1KWuqQrR2C/slYSZMC+QcvQ5TIR+59Arps0lCb1tT44TRsub7PcG0YuL63Ibbon7/jRCuKn54GUSKfId04WtDXtgV0Nexsqlf9bVIopd/RUods7RD0T8YqGiS4vCcf/BQRBFm5dNmkoTatqfHDadhyfZ/h2jBwfW9DbNE/v7V0B6ZtLAY/RcSQbrQ3xOG7EJRT1odq4lWan8RQtzK0o/q+ADDqgaNvT9lJ1iWjXNrDaBBGOTu28YYatqJEvtnSy6Jv2hjPp+YuyIpm1OZzfW974U6CKraGcsr6UE28kl4oZahbGdopGiS06pVhDxx9e8oueZsEPafLGQ3CKGfHNt5Qw5afIjJbetl2pIjxfGru6OwCRm0+1/e2F85qhUEcvgtBOT1KLxbQ5ZYXv3sBxy52046Psju2ejpmResing7lLaS/cQbry5qNHPbZjj663z6lgSt66zwKsqKNrrONN6dhy4YlWr1c35uN0eysbQHl9Ci9WECXW77wymJ0nz5GOz7KbvqLx8CLnwVA18L3zJ/T0bx/vZHD7ms5S/fbpzRwz28TITq7wOg623hzGrZsWKLVy/W92XDnvkUkh+9CUGmW8ovdqL6qhEqtwaxoHjq2ZzAaj3Vsz0DH9gxM4vtD2tkPSZMCJfVdJp/LpoELMHV0h3OqXDRsDbFEq5frexNsD5Vm6a4rh/JSNTQDKvDiZyHjQAej8VjGgQ5kHOiAf9gk9LdJoWiQoKuqxORz2TRwAaaO7nBOlYuGrSGWaPVyfW9Pg0T4LkTh/BhImhSMvLypnLdhumQ4TLVY5qqwxUXD1hBLtHoteW9DPE0D19HELCuEokHCyMubynkbpkuGw1SLZa4KW1w0bA2xRKvXkvc2xJ01cInDdyGSIgPRsT2DsSFL9WMvnB9Dp0BK6mXYXdWOvDQBFt87AfwAH0TwxiKl6LST38A6uL43wfYExiQh40AHY0OW6sces6yQToHIvtWlVATz8jBh9mL4BPExNiQCp/+U4uQ3sA6u7+1pEIfvgiRFBiIpMhDZ907AtR41coqlkDQp6Ei18HOdnqd+dM1FztBauGrY6mONVq+592ZdG4nebUJgTBICY5IwIS0batk1SHflQNEgoSPVlg91G5760TUXOUNr4aphq481Wr3m3psNV43euUBy+C7E5vIWCLfW4Ey77j+SMMQPsaGmVaIox0tthtoLrhq2+lii1WvpexNsR4t4M2pWCaFq0UlW+oUK4S+INWlPOV5qM9RecNWw1ccSrV5L39tTIBG+C5GTGg5xnQyL371gdK9oya/RPCUM/sCes6zPaelWG+XdRwpXDVsKS7R6ub43wfaEZ+ZAVinGhVcWG92LX1FEf00Jg5/96wOsz1HLWozy7iOFq4YthSVavVzf29MgDt+FmBXNM6qPL8iKxkxhEENMfOmMMPTd1tCpnYKsaCxPCYd6UAPRW+dRc01pU4dviYat4TguWr1c35tge3jxs4zq46OzCxAUP5MhJh6WvhQadR+d2onOLkD4/cuhua3G+W0iKJtqbOrwLdGwNRzHRauX63t7GkTT1oZ4mqatNRq2BCZE09YyrNGwJRhDNG0JBAJhlEMcPoFAIIwSiMMnEAiEUQLZtCWYhOTuCY6G5O7tC4nwCQQCYZRAInwPxF2rawz74rCtn2qlPNJ3U6k1+PyinG7ZTOkJ6Pf557Iegg53ra4x7ItDrV8zoIK87nMozknolgthc5dx7slvDqo9s+HPy9R6bAVx+AS3wZaas6/8o5U+GwCA4fjN9fkneD6tZa/QdfzAr331ufTkN4ejdWz1IQ6f4HKwRdJUf3xbIO3sh7hOhoKsaOTeF0H3+d/7XQfEdTL6pDK1Di4dOQnujX4k3d8mhaxSjOjsAkRk5dI9+Tsq9kJWKR7RqWKq5765dXDpyGkNJIdPcHlsrTl7tqMPALA8JZzR5/+Z2QIAQOP1PpvMQ3BP+q7qWpaE37+c0ZNfMO8Z3f3WRque6ywdW31IhO8CCLfWIC9NwNpbfnN5C6PrJBctW7bnA8aRs6nrXHVoTc0zHNbkwW2tOduhvA0ACDfQCYjgjQUANN0YGPEcrk7NKiEE8/JYe8u3iDczuk5y0bJlez5gnIM2dZ2rDq2peYbD0jz47W6dvW9wOOP62BCd8M9AR5NFz6Nwlo6tPsThuwBbFk7GjhOteOHBGEaPGXn/IMR1MmxZOBk8f2+LtWytgasOrSOx9WYp9X6GbZupn/3uqnanvaujmJyzBa2lOxCz9AVGj5nBXjlklWJMztkC7wCexVq21sBVh9ZRUGsx3Jylfk7t5butWpcrbGgTh+8CPBCvi56rW5QMp119VwZQlBgKwHItW0uxRIeWDVLF4j6EJOm6XiovVzOctvKuTGBoqq6BmKVatpZiiQ4tG67gRN0JksN3AZIiAyFK5ONIo5xx/UijHHlpArrzpaVatpZiiQ4twb0JjEkCP0UE+ckjjOvyk0cgmJdHb0paqmVrKZbo0BJGDonwXYTVGVHIKZbSFSIt3WpImhQozWdGN5Zo2VqKJTq0bBB9WfciSrQa0l05dNWJWtYCRYMESS+UMuws0bK1FEt0aNlwZ31ZZ0AifBchOSoIAFBzTRdFU5Ui1HWAqWVbmp8EybpkNBTOdvxi3RxKmtFQFpL6fjjpRk8iKDYZAKBs0v2hpqpPqOsAU8s26YVSJG+TYPabDY5frAOhZBQNJRyp74eTWXR1SITvIvD8vVG0JB6Fn7dg4bRQrC9rRtGSeMbGoi21bPVlByms0aHVx12i98TwAADAjf5Bxnu23dRJRgpDxjplXY7GO4CH+BVFaPmwEKGpC9G8fz3iVxQxNittqWWrLztIYY0OrT72iN4DhIkAgMHeG4w1qeVtAICxE+xTI+8ISITvQmTE6jZvU4pOAwDmTR3Pamepli2lGkVpxqrUGrx/qtPIzhIdWncm4a7DL2u4wdDqLZf2AABmCoNMjvU0QhJ1f6RP/ykFADB++jxWO0u1bCnVKEozVjOgQufX7xvZWaJD6ygConTnPW58X8bQ1e05XQ4ACIqb6fA12QoS4bsQ8RP86Sg7L01AHwqisFbLdtmMMEiaFIyTqlsWTjays0SH1hXh2kOI2iRne8+8NMGwlUiehr8gno6yBfPy6INGFNZq2YbNXQZFg4RxqnRyzhYjO0t0aB0FtaHNtibBvDxG1ZC79RAiEb6LQUXZOanGznXpjDCGqHdBVjS+2zgTknW6nCuV/2cbt295Ah3pFy2JN7n5Wjg/BvuWJyAvTUBfK1oSj11LpzDOCLg7u5ZOQdGSePpnIkrko2hJPF582PgPoadDRdnhmTlG98LSlzJEvaOzCzDz1e+QvE1Xh0/l/9nGJazdR0f68SuKTG6+xiwrRMLafRDMy6Ovxa8owpT8XYwzAo5kSv4uxK8ootfPTxEhfkURJi9/0SnrsRVE09aGeJqmraOxRZdP4dYam+8ljGRdRNPWtbFFhF6zSmjzn/9I10U0bQkez5l2FeMTEIFgb1QtZxifgFwdksMnuBzWRtR1P6mGPSdg7ToIno+1EbWquW7YcwLWrsNekAif4DHY0tkTCFywpbN3BCTCJ7gMrlbH72rrIdgeV9v7sPd6SIRPIBAIowTi8EcRwq01Hp2XVqk1KKmXIf/gZQi31iD/4GUcbZRbfRqZMHJqVgntnpd2FRQNEpd/V5LSIXgMRKeW4CycqVNrCcThEzwCrjq1BIKtMadT60qQlA7BIyA6tQRn4Ao6tZZAInwPQaXW4JtmBY40yiFpUiAvTYA1GRPNRrVcNXL1dW4BXVsHNgUsrnaGjLSXPtGpdTyaARUUjd9AfvIIFA0SCOblYeKCNay9dfThqpGrr3ML6No6sClgcbUzxBa99F1Bp9YSiMP3EDZ81kxr2wKAuE4GcZ0MknXJJp0tV41cNjuq8VhpfhItcM7Vzh4QnVrH0/zuBlrbFgBklWLIKsVI3iYx6Wy5auSy2VHNzJJeKKUFzrna2QtXK+s0B3H4HoC+0DglT3i0UY71Zc346LSM0T9fH64auZRd7fOz6HTJmXYVFr97AccudtOOnKsdG6Tm3b3QFxqn5AnltUfRvH89ZJUfMfrn68NVI5eym7Wzlu7gSeXKu08fox05Vzs23M1Z2wLi8D2Ar5t1EfnKOZF0hLuUJS1jCOVk5f2DkHb2o0N5m86F6yNK5EPSpED5xW5MjwpEclQQZkXzjJw0VzuC+6M4/zUAIPKhlbRISFj6UrOi5pSTHeyVo79Nits9HehrMW73zU8RQdEgQXddOQInTUdQbDJ48bOMnDRXO4IO4vBtzK07jm8+SuXLrWlfzEUjt3B+DCRNCkaef3VGlFHEztWODU/Ww/X2tl9thJ+f7pOUdkgDrzGWq5RZC5Uvt6Z9MReN3JhlhVA0SBh5/ijRaqOInasdG56qh6sd0p07oX439CEO34bweDy03Xb2Krijr5G7+N4J4Af4III3llbcokiKDETH9gzGBi9V3144P4beI+BqZw8o4RaVWsPI4ztbp7ZXfQchwZZL93ElJET3x1QzoIJPILtCmiuhr5E7YfZi+ATxMTYkglbcogiMSULGgQ7GBq+iQQJ+iggxywrpPQKudqMJzS+9AIDx441/H4jDtyFTpkxBxeFbDp+XUsmS9w9aFOVbqpGbFBmIpMhAZN87Add61MgplkLSpDCKvLna6TPS6N1VdWqvKdSYEm+/ls1Tp04FAKi7riEoLtVu8xhCqWQN9sotivIt1cgNjElCYEwSJqRlQy27BumuHCgaJEaRN1c7fdwxeueC+obuE3ZCgrEuB6nDtyFpaWlo7+ljFQi3JxmTgwEA75/qpB320UY5hFtrsLm8xex4cxq5m8tbINxaQ2viCkP8EBtqXO7J1c4euKpObaPsFtLm2C8VFRsbi9CwcPRda7DbHGwE39XC7fz6fdphy2uPomaVEC3izWbHm9PIbRFvRs0qIa2J6xcqhL8g1mq70UTftQaEhoVj0qRJRvdIhG9DMjMzEcILwjc/3ETOTMfpvy6dEYYjjXJWjVbq4BEbXDVyc1LDIa6TMTRxKfQFR7ja2QNX1KlVDtzB6WtKvLRokV3nyV60CMfPfY3IB1fYdR59wtKXQn7yiAnd12dMjuOqkRuemQNZpZj1BKu+4AhXu9GEqvFrZJv4nSMRvg3x9fVF7u/zUNqoMG9sY/b+NoFV73Y4R8dVI3dWNA+SdcmMPHhBVjSKn56G3Pt+/YPC1c5euJpO7ZFGOcLCQrFgwQK7zvOH/BXouVCF2zdl5o1tSMLqvax6t8Plzblq5PLiZyF5m67sU9922sZiCP4ll77G1W60cPumDD0XqrDyD/ms94mmrY25cuUKpicloSw/ka5tJ4w+bt8Zwvz9l/Dcpq14/vnn7TqXVqvF7DkZ6JqQipgntth1LoJr03Z4ByK6z+H0qRp4eXkZ3ScRvo2ZOnUqNhZsxH+cuI4h8qd01PLOyU748iZgw4YNdp/Ly8sL+/buRufXH2Dg5yt2n4/gmgz8fAWdX3+A/97zd1ZnDxCHbxde+o8t+HnACwdO/uzspRCcwBX5APZ89zPe2L0Xvr6Wn42whjlz5uB3Tz6Fnz4uhPaOY4sGCM5He2cQP31ciN89+RTmzp1r0o6kdOzE4cOH8dSTT6L46XswP4Hv7OUQHER3/yCWftCEWQ88hLJPjzh07s7OTiRNT4b/jEcQa6K1AcEzuSbeDHXjl5BeOI/IyEiTdiTCtxNPPPEE/lz4Z/z7Z1ch7ex39nIIDkA9OIQ1ZS0IiojBR+ISh88fGRmJLyqOobvmMDqO73X4/ATn0HF8L7prDuOLimPDOnuAOHy78uqrr0G08FE8/uFlVP1409nLIdiRrr5BLP+oCW0DY3H8yxMYN26cU9YxZ84cvH/gPbR99jf8dPhlQDvklHUQHIB2CD8dfhltn/0N7x94D3PmzDE7hDh8O+Ll5YVPDpUi56nf45mSJhTXdoIk0DyPi539WHzgEm6NE+C76u8RHe2cNg4Uubm5OHToEOSVxbjy1hpo1ET8xdPQqPtw5a01kFcW49ChQ8jN5VaCSnL4DmLnzp3YvHkT0iePx/aFMZge5fiDQATb0qu+g53/146P6mSY/+CDKC37lO5t4wrU1tZi0eKl6B/UYuK/bkJEZg5gonqD4CZoteiqLsX1//0bAn29UHHsKNLT0zkPJw7fgdTX1+O59c+itq4ey1PDsWpOJHH8boi8fxD/c6YL+092wdtvHF4v2on8/HyMGeN6H5jlcjm2bN2Kd/a/A15cMiJEzyJ05kJ4eZND9u6EVnMHPWdPoEvyNlRXz2PN2jXYsX07wsIs61ZKHL6D0Wq1+OSTT/DaKy/jgvQS4sJ5uH/SOCSEB4Af4AN/X9dzGqMdzZAWSrUG13rUOPezGqdbb2J8CA+r167Dpk2bWLsSuhrnzp3D1m3bUV5+DL4BQeAlZiJg0nSMDYmg+9kTXAvNgAq3b8ow0HYRqsv/xKC6H9nZi7F921akplrXKI84fCdSX1+P48eP49uqSkgvXkCP4ibUt9yov/IoYcwYL4wPCcaU+HikzcnAo48+iocffhj+/o5pDGdLfv75Z5SXl0Mi+QdOnz2HLlkn+lW9zl4WgYVAXjAiBJGYPTMVItHDyM7ORlRU1IieSRw+gUAgjBJI/oBAIBBGCcThEwgEwiiBOHwCgUAYJfgAsG/vVgKBQCC4BP8PRAdWrDvXeyYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = pydotplus.graph_from_dot_data(dot_dat)\n",
    "display(Image(graph.create_png()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到 sklearn 中实现的分类决策树是 CART 树，是二叉树，对于离散值还是按连续值处理的，这样的作为会默认离散值的数值有意义，决策树特征选择时会按照数值排序切分。所以对于类别特征一般都需要做处理\n",
    "- one-hot: 失去数值意义，标记类别\n",
    "- label encoding：以有意义的数值来代替类别特征，比如以人均消费代替城市类别\n",
    "\n",
    "参考：\n",
    "https://scikit-learn.org/stable/modules/tree.html#tree\n",
    "\n",
    "其中有描述 scikit-learn uses an optimised version of the CART algorithm; however, scikit-learn implementation does not support categorical variables for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
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